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What is Data Mining

Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
Use of computational intelligence and/or statistical methods to dig out patterns in large data sets.
Published in Chapter:
Challenges and Opportunities of Soft Computing Tools in Health Care Delivery
André S. Fialho (Massachusetts Institute of Technology, USA), Federico Cismondi (Massachusetts Institute of Technology, USA), Susana M. Vieira (Technical University of Lisbon, Portugal), Shane R. Reti (Harvard University, USA), João M. C. Sousa (Technical University of Lisbon, Portugal), and Stan N. Finkelstein (Massachusetts Institute of Technology, USA)
DOI: 10.4018/978-1-4666-3990-4.ch016
Abstract
During the last decade, modern hospitals have witnessed a growth in the amount of information acquired, stored, and retrieved more than ever before. While aimed at helping healthcare personnel in providing care to patients, this high stream of data can also have a negative impact if not delivered in a simple and organized way. In this chapter, the authors explore the current opportunities and challenges that soft computing predictive tools face in healthcare delivery, and they then present an example of how some of these tools may contribute to the decision-making of health care providers for an important critical condition in Intensive Care Units (ICU)—septic shock. Despite current challenges, such as the availability of clean clinical data, accuracy, and interpretability, these systems will likely act to enhance the performance of a human expert and permit healthcare resources to be used more efficiently while maintaining or improving outcomes.
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Challenges and Opportunities of Soft Computing Tools in Health Care Delivery
Use of computational intelligence and/or statistical methods to dig out patterns in large data sets.
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Studying Educational Digital Library Users and the Emerging Research Methodologies
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Association Rule Mining
Extraction of interesting, non-trivial,implicit, previously unknown and potentially useful information or patterns from data in large databases.
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Business Ethics in the Information Age: The Transformations and Challenges of E-Business
The use of often sophisticated computational means of gathering information about persons or other subjects through the analysis of various forms of digital data.
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Multi-Level Data Integration and Data Mining in Systems Biology
The process through which large amounts of data are sorted with the aim to extract from them relevant information. This term is increasingly used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods, especially in the biological context. It can be defined as the nontrivial extraction of previously unknown and potentially useful information from data and databases.
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Machine Learning
The process of extraction of useful information in large databases.
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Application of Data Mining Techniques in Clinical Decision Making: A Literature Review and Classification
Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
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Assessment of Higher Order Thinking Skills: Digital Assessment Techniques
The process of discovering hiding patterns in large data sets to discover useful information.
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Teaching a Data Mining Course to MBA Students
The computational process of collecting and analyzing large amounts of data to identify relationships, patterns, trends and other useful information that can be used to predict future behavior.
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Intelligent Processes in Automated Production Involving Industry 4.0 Technologies and Artificial Intelligence
Is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
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Virtual Singers Empowered by Machine Learning
Extraction of patterns in datasets.
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Concept of Automated Support to Problem: Modular Vocational Training
A computer process that uses artificial and machine learning methods to discover hidden information or relations in large amounts of data.
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Outlier Detection in Big Data
The process of analyzing data from different perspectives to predict future behavior and trends.
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Visualization and Storage of Big Data for Linguistic Applications
Data Mining is a field in knowledge theory that develops and uses tools for retrieving significance from the Big Data. This methodology strives to find a common denominator among some parts of the data. There are two main properties of data: an individual property related to the separate elements of data or a related property of the relationship among some elements of data.
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Data Mining and Knowledge Discovery in Databases
One of the phases of the KDD process and concerns, mainly, to the means by which the patterns/models are extracted and enumerated from data. Many times is identified with the complete KDD process.
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Identifying the Key Success Factors of Innovation for Improving the New Product Development Process
The process of discovering potential useful patterns and relationships in large amounts of data.
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A Review of Advances in Supply Chain Intelligence
Set of knowledge discovery techniques for intelligent data analysis in order to find hidden patterns and associations, devise rules and make predictions.
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Reading Data Possibilities From an LMS Data Portal Data Dictionary
Identification of patterns in data in the pursuit of information and insights.
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Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models
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Handling Fuzzy Similarity for Data Classification
The process of automatically searching large volumes of data for patterns, using tools such as classification, association rule mining, clustering, etc
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Combination of Forecasts in Data Mining
The process of selection, exploration, and modeling of large quantities of data to discover regularities or relations that are at first unknown with the aim of obtaining clear and useful results for the owner of the database.
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Educational Data Mining and Learning Analytics in the 21st Century
It is also known as Knowledge Discovery in Databases (KDD) and refers to the use of algorithms, techniques, and methods in order to generate knowledge by discovering novel and useful information, patterns, relationships or structures from large data collections.
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A Novel Approach to Segmentation Using Customer Locations Data and Intelligent Techniques
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Misuse of Information Technologies and Reliability of Information in New Media during Emergencies
A technologically-driven process of using algorithms to analyze data and extract meaningful patterns that can be used to predict behavior.
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Strategic Approach to Business Intelligence and Its Impacts on Organizational Performance
The term used in computer science. It is the process of accumulating a large amount of information and screening useful information. It is also called knowledge discovery in databases (KDD).
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Using Data Mining Techniques to Predict Obstetric Fistula in Tanzania: A Case of CCBRT
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A Hybrid Intelligent Risk Identification Model for Configuration Management in Aerospace Systems
An area of Artificial Intelligence related to the Knowledge Discovery through exploiting big quantities of data.
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Data Analytics in Industry 4.0: In the Perspective of Big Data
Methodologies that contribute the extraction of information among large scale data.
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Duplicate Journal Title Detection in References
Data mining is an iterative process of searching for new, previously hidden, and usually unexpected patterns in large volumes of data.
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Heuristics in Medical Data Mining
Analysis of data using methods that look for patterns in the data, frequently operating without knowledge of the meaning of the data. Typically, the term is applied to exploration of large-scale databases in contrast to machine-learning methods that are applied to smaller data sets.
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Decision Trees
Process of automatically searching large volumes of data for patterns.
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The State of the Art in Web Mining
The process of extracting useful information from large datasets.
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Threat Detection in Cyber Security Using Data Mining and Machine Learning Techniques
The application of specific algorithms for extracting useful patterns from data for insight.
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Implementation of Different Machine Learning Projects Using Scikit-learn and Tensorflow Frameworks
It is a technique of discovering correlations, patterns, or trends by analyzing large amounts of data stored in repositories such as databases and storage devices.
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Fuzzy Classification in Shipwreck Scatter Analysis
Data mining, also called knowledge discovery in databases or knowledge discovery and data mining, is the process of automatically searching large volumes of data for patterns using tools such as classification, association rule mining, clustering, and so forth. Data mining is a complex topic, has links with multiple core fields such as computer science, and adds value to rich seminal computational techniques from statistics, information retrieval, machine learning, and pattern recognition.
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Distributed Programming Models for Big Data Analytics
Extraction of interesting, non-trivial, implicit, previously unknown and potentially useful patterns from large dataset.
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Big Data Analytics and Mining for Knowledge Discovery
Non-trivial extraction of implicit, previously unknown and potentially useful information from data.
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Machine Learning and Deep Learning for Big Data Analysis
Finding relevant patterns, trends, and information inside huge databases by statistical, mathematical, or computational methods is known as data mining.
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A Neuro-Fuzzy Partner Selection System for Business Social Networks
Data mining, a branch of computer science, is the process of extracting patterns from large data sets by combining statistical analysis and artificial intelligence with database management. Data mining is seen as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and business partner selection.
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Bankruptcy Prediction Using Data Mining Tools
Finding out meaningful patterns from large amounts of data.
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LRFM Analysis as a Customer Segmentation Tool in the Tourism Sector
The task of accessing and using meaningful data from databases where a lot of information is stored for purposes of making predictions.
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Artificial Intelligence and Machine Learning Algorithms
In general terms, data mining is a process of finding several new patterns in a huge collection of data sets using numerous techniques like classification, clustering, regression, etc. to predict future trends.
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Data Science in Economics and Business: Roots and Applications
The process of finding differences, patterns, and correlations within large data sets to predict outcomes.
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Mobile User Behaviors in China
Data mining is an efficient method to process large-scale data and has become a widely used technique in behavior analysis. By data mining, researchers can extract some useful features from massive data. When applied to mobile user behavior analysis, data mining can be helpful in extracting features such as preferences by using the data collected in mobile phones.
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Twitter Data Mining for Situational Awareness
A computational process of discovering patterns in large data sets. The main goal is to extract knowledge from a data set and transform it into an understandable structure for further use (i.e. machine learning or predictive analytics).
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Web Mining: A Conceptual Overview on Intelligent Information Retrieval Systems
(Semi-)Automatic and systematic exploration and extraction of unknown information which accrues within large data-pools.
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Ant Colonies and Data Mining
Important branch in industry and market, retrieving important information from a huge amount of data. It is usually considered with huge amount of heterogeneous data, where the use of computers is inevitable.
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Bicluster Analysis for Coherent Pattern Discovery
Data mining is the process that attempts to discover previously unknown structures in a large data set such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining).
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The Adoption of Business Intelligence as a Competitive Strategy Among SMEs: A Developing Country Study
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Digital Forensics and Data Mining
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
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Demand Forecasting Models With Time Series and Random Forest
A set of machine learning and statistical methods or techniques for drawing insight or extracting information from data or revealing patterns that are not implicitly available.
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Data Mining: Payoffs and Pitfalls
Also known as knowledge discovery in databases (KDD), data mining is the process of automatically searching large volumes of data for patterns. Data mining is a fairly recent and contemporary topic in computing.
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“Smart City”: The Concept of Resolving the Contradiction Between Production and Urban Life
The computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
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Auditor Change Prediction Using Data Mining and Audit Reports
Classification or prediction is the most common data mining method, which uses a set of pre-tagged datasets to train the model for future prediction. Fraud detection, auditor changes, and risk analysis are classic examples of applications in this technology.
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A Review of Advances in Supply Chain Intelligence
Set of knowledge discovery techniques for intelligent data analysis in order to find hidden patterns and associations, devise rules and make predictions.
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Discovering Behavioural Patterns within Customer Population by using Temporal Data Subsets
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The Role of Social Media Use in Health Communication: Digitized Health Communication During COVID-19 Pandemic
It is the analysis and processing of data from the data repository and trying to produce meaningful information.
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A Preliminary Framework to Fight Tax Evasion in the Home Renovation Market
Data analysis technique allowing extraction of new information, hidden correlations difficult to see under a mass of data, trends, anomalies, associations, mainly by the use of statistical processing, often in the context of big data.
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A Brief Overview on Intelligent Computing-Based Biological Data and Image Analysis
It is the process of determining hidden patterns from the dataset using various methods like machine learning, statistics, etc.
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Educational Data Mining: A Guide for Educational Researchers
The techniques used to acquire, process, analyze and report meaningful results from large datasets.
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Knowledge Management in E-Government
The use of algorithms to automatically search through massive data stores from different sources in order to find unknown patterns and interrelationships that ascribe meaning to the data.
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The Interdisciplinary Fields of Political Engineering, Public Policy Engineering, Computational Politics, and Computational Public Policy
Data mining is the science of sorting through data to establish relationships and identify patterns.
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Integrating Imaging and Clinical Data for Decision Support
The principle of sorting through large amounts of data and picking out relevant information.
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Air Pollution Monitoring in Intelligent Cities Using Weighted Association Rule Mining
This is the process of discovering useful information and trends in a given data set by extracting hidden patterns through applying the mechanisms of classification, clustering and association rules analysis.
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Frequent Itemset Mining and Association Rules
One step of the KDD process. Can include various data analysis methods such as decision trees, clustering, statistical tests, neural networks, nearest neighbor algorithms, and association rules
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Disruptive Technologies and Education: Is There Any Disruption After All?
Data mining is fundamentally a way to search for useful information through large amounts of data being generated with the facility of information technologies. Data mining is also often referred to as ‘analytics’ or ‘knowledge discovery’ because its objective is precisely to generate knowledge or discover patterns of information amongst that data from which useful knowledge can be obtained. The actual mining process is made by software featuring artificial intelligence techniques like ‘machine learning.’ Data mining has become an important complement of other data analysis tools because the amounts of information available these days sometimes are impossible to be analyzed with traditional methods; however it has also become a buzzword which is often miss-used.
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Adaptive Business Intelligence
The application of analytical methods and tools to data for the purpose of identifying patterns, relationships, or obtaining systems that perform useful tasks such as classification, prediction, estimation, or affinity grouping.
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The Applications of Simulation Modeling in Emergency Departments: A Review
The fact of dealing with big data where new information can be generated from pre-existing databases.
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Learning and Performance Innovation
The process of extracting patterns from data and an important tool used to collect useable information.
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Big Data Mining and Analytics With MapReduce
Non-trivial extraction of implicit, previously unknown and potentially useful information from data.
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Clustering Methods for Detecting Communities in Networks
The process of exploring large amounts of data in search of consistent patterns.
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Data Mining and Knowledge Discovery in Databases
One of the phases of the KDD process and concerns, mainly, to the means by which the patterns/models are extracted and enumerated from data. Many times is identified with the complete KDD process.
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Big Data, Artificial Intelligence, and Their Implications in the Tourism Industry
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Data Science Methodology
Data mining is the key process step of analyzing the data to gain insights that can then be used to achieve the defined goals. The application of algorithms and statistical models is part of data mining.
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Mining User Activity Data In Higher Education Open Systems: Trends, Challenges, and Possibilities
In common parlance, data mining often refers generally to the idea of probing deeply into some mountain of data. This informal use of the term usually says little about the techniques used to do the probing. In contrast, the more formal use of the term refers specifically to using computational techniques to uncover patterns in huge data sets. Here the techniques range widely from statistics to artificial intelligence. The range of data mining investigations is also varied and ever increasing, but some of the better-known approaches include clustering, classification, and affinity analysis.
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Leadership for Big Data and Business Intelligence
Sometimes called data or knowledge discovery, data mining is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified.
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Different Approaches for Cooperation with Metaheuristics
The most characteristic stage of the Knowledge Extraction process, where the aim is to produce new useful knowledge by constructing a model from the data gathered for this purpose
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A Comprehensive Analysis of Data Mining Tools for Biomedical Data Classification: Assessing Strengths, Weaknesses, and Future Directions
The process of searching and analyzing large amounts of raw data to identify patterns and relationships that yield valuable outputs.
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Data Mining for Visualizing Polluted Gases
Is the process of turn raw data into useful information, by finding relationships between variables, especially in big data.
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Quality and Effectiveness of ERP Software: Data Mining Perspective
Refers to the process of extracting patterns, trends, and knowledge from a pool of an organization’s data using algorithms.
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Big Data Mining and Analytics
Refers to non-trivial extraction of implicit, previously unknown and potentially useful information from data.
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Only Can AI Understand Me?: Big Data Analytics, Decision Making, and Reasoning
Is a process of discovering various models, summaries, and derived values, knowledge from a large database. Another definition is that it is the process of using statistical, mathematical, logical, AI methods and tools to extract useful information from large databases.
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Promoting Social and Solidarity Economy through Big Data
A set of techniques to extract patterns from large datasets by combining methods from statistics and machine learning with database management.
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Robotics E-Learning Supported by Collaborative and Distributed Intelligent Environments
Is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
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A European Virtual Enterprise on Collaborative Data Mining and Decision Support
Mainly concerned with analyzing existing data, typically stored in a database or a data warehouse. It is the core of a knowledge discovery process, which aims at the extraction of interesting, non-trivial, implicit, previously unknown, and potentially useful information from data. It is an interdisciplinary area involving databases, machine learning, pattern recognition, statistics, and data/model visualization.
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Techniques and Tools for Big Data Analytics in the Tourism Sector
The process of discovering patterns and relationships within tourism datasets to identify trends and valuable information.
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Customer Relationship Management and Data Mining: A Classification Decision Tree to Predict Customer Purchasing Behavior in Global Market
Is the process that uses soft computing techniques to extract and identify useful information and subsequently gain knowledge from large databases.
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Terminals for the Smart Information Retrieval
Is the principle of sorting through large amounts of data and picking out relevant information.
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Mining Frequent Closed Itemsets for Association Rules
Extraction of interesting, non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases
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Analytics for Noisy Unstructured Text Data I
The application of analytical methods and tools to data for the purpose of identifying patterns, relationships or obtaining systems that perform useful tasks such as classification, prediction, estimation, or affinity grouping.
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The Role of Data Mining in Intrusion Detection Technology
This is the process of automatically searching large volumes of data to uncover previously undetected relationships among data items. Data mining is also known as knowledge discovery in databases (KDD).
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Real-Time Bidding Advertising: Challenges and Opportunities for Advertising Curriculum, Research, and Practice
The term is also known as data or knowledge discovery. It describes an interdisciplinary sub-field of computer science to describe the computational process of pattern discovery in large datasets through the analysis of data to generate knowledge and intelligence for business decisions.
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Research Trends in Information Systems From the Management Discipline Based on Co-Occurrence Analysis
Is the technology that allows relationships and trends to be discovered through the analysis of amounts of data stored in databases.
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Process-Based Data Mining
Analysis and nontrivial extraction of data from databases for the purpose of discovering new and valuable information, in the form of patterns and rules, from relationships between data elements.
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Assessment of Machine Learning Techniques for Improving Agriculture Crop Production
Data mining uncovers valuable insights for informed soil classification and crop choices. It identifies trends, such as soil characteristics' impact on crop yield.
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Motives and Methods for Quantitative FLOSS Research
Collecting information in order to use that collected information for a specific purpose.
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mHealth Environments for Chronic Disease Management
The process of automatically discovering useful information in large datasets. Data mining techniques such as classification, cluster analysis and association analysis provide capabilities to find useful patterns and predict the outcome of a future observation
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Measuring the Attitudes of Governmental Policies and the Public Towards the COVID-19 Pandemic
Is an interdisciplinary area that includes fields such as artificial intelligence, database, statistics, machine learning, developed to discover hidden patterns, and meaningful relationships from large databases.
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The Nature of Intelligent Analytics
Is a process of discovering various models, summaries, and derived values, knowledge from a given collection of data. Another definition is that it is the process of using statistical, mathematical, logical, AI methods and tools to extract useful information from large databases.
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A Study of Information Requirement Determination Process of an Executive Information System
It is an information extraction activity whose goal is to search large volumes of data for patterns and discover hidden facts contained in databases.
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Internet Diffusion in the Hospitality Industry
The process of analyzing data to determine relationships undiscovered by previous analyses.
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Application of Data Mining in Smart Grid Technology
A process of extracting information from raw data sets.
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Big Data in Massive Parallel Processing: A Multi-Core Processors Perspective
Computational process and technique of discovering useful patterns in a large dataset.
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Enterprise Data Lake Management in Business Intelligence and Analytics: Challenges and Research Gaps in Analytics Practices and Integration
Techniques and processes are also used in big data analysis and business intelligence to provide summarized, targeted, and relevant information, knowledge for the user is autonomously generated from large amounts of data.
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The MOOCs: Characteristics, Benefits, and Challenges to Both Industry and Higher Education
The selling of college student information and their academic performance to potential employers. In addition, students with exceptional academic performance and achievement may be sold to colleges and universities around the world.
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The Impact of the Impact of Meta-Data Mining From the SoReCom “A.S. de Rosa” @-Library
Application of specific algorithms for extracting patterns from data; the term primarily used by statisticians, data analysts and the management information systems (MIS) communities who find useful patterns in data.
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High Performance Computing, Big Data, and Cloud Computing: The Perfect De Facto Trio or Converging Technological Mantras?
Process that tries to discover patterns in large volumes of data sets. It uses the methods of artificial intelligence, machine learning, statistics, and database systems.
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Categorization of Data Clustering Techniques
Data mining is a knowledge discovery process that focuses on extracting previously unknown, actionable information from very large databases.
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A Knowledge-Based Recommender Framework to Recommend Destination Country for Human Migrants (KBRF): The Case of Ethiopia in Africa
It consists of a set of processes to extract knowledge and insights from the data associated with real-world application, i.e. human migrant recommendation application.
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Using Deep Learning and Big Data Analytics for Managing Cyber-Attacks
Finding patterns and other valuable information from huge mountains of data sets is known as Data Mining which can also be referred as knowledge discovery in data (KDD).
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Learning Analytics
Field of knowledge aimed at the study of techniques to automatically analyse and extract meaningful information from large datasets. There is some overlaping between Data mining and Artificial Intelligences, and some techniques can be included in both research fields (e.g. cluster identification).
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Different Roles and Definitions of Spatial Data Fusion
The analysis of data to establish relationships and identify patterns.
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Using Business Analytics in Franchise Organizations
Analytical techniques used to find out the hidden relationships or patterns residing in the organizational data.
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Analytical Review of the Applications of Multi-Criteria Decision Making in Data Mining
DM is one type of machine learning. We do not know the rules but the machine (computer) learns by discovering these rules from data ( Alpaydin, 2016 , p. 14). In other words, DM is the study of collecting, cleaning, processing, analyzing and extracting useful insights from data ( Aggarwal, 2015 , p. 1).
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Modelling and Assessing Spatial Big Data: Use Cases of the OpenStreetMap Full-History Dump
Is the process of discovering patterns in large datasets involving methods at the intersection of machine learning, statistics, and database systems.
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Mining Association Rules
The process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases.
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A Survey on Data Mining Techniques in Research Paper Recommender Systems
The practice of examining large pre-existing databases in order to generate new information.
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Association Rules Mining for Retail Organizations
Analysis of data in a database using tools which look for trends or anomalies without knowledge of the meaning of the data. The nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The science of extracting useful information from large data sets or databases.
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The Road to Digitally-Driven Mental Health Services: Remote Psychological Interventions
An automatic or semi-automatic process of extracting, analyzing, and discovering patterns in large scattered data sets involving methods at the intersection of machine learning, statistics, and different software enabling companies to convert raw data into useful information (e.g., patterns, anomalies) that can be used for multiple settings and purposes.
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A Case-Based-Reasoning System for Feature Selection and Diagnosing Asthma
Data mining, also called knowledge discovery in databases, in computer science, the process of discovering interesting and useful patterns and relationships in large volumes of data. The field combines tools from statistics and artificial intelligence (such as neural networks and machine learning) with database management to analyze large digital collections, known as data sets.
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Technological Approaches to Maintaining Academic Integrity in Management Education
The process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management
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Machine Learning in the Catering Industry
Recognition of patterns in datasets.
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Understanding Shopping Behaviors With Category- and Brand-Level Market Basket Analysis
Data Mining is a research field, and a set of methods that are developed with intention to extract interesting and useful patterns from existing data.
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Data Mining for Fraud Detection
Finding insights which are statistically reliable, unknown previously, and actionable from data.
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Big Data: An Emerging Field of Data Engineering
Data mining is the analysis of data for relationships that have not previously been discovered.
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Integration of Data Mining and Business Intelligence in Big Data Analytics: A Research Agenda on Scholarly Publications
Is the practice of examining large pre-existing (structured) databases in order to generate new information. The data warehousing platforms should be established incorporation level with necessary data marts to analyze the large data sets.
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Challenges in Data Mining on Medical Databases
Data mining is one step in the knowledge Discovery in Databases (KDD) where a discovery-driven data analysis technique, such as Naïve Bayes or Neural Networks or Association rules, is used for identifying patterns and relationships in data sets
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Learning Styles in Online Environments
The process of discovering knowledge in databases by identifying patterns and trends in data collected using classification, association, and clustering rules.
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Analysis of Big Data
In large amounts of textual or mixed visual and textual data sets, data mining is a process of searching, extracting, and analyzing (that may include) exploring multiple kinds of text graphic patterns (as calligraphic for example), language and literary figures, stylistics, that also includes techniques at the intersection of machine learning, formal linguistics analyses as textual statistics, and database systems.
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A Hybrid Classification Algorithm and Its Application on Four Real-World Data Sets
The computer-assisted process of analyzing dense volumes of data and extracting interesting and useful information.
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Credit Risk Assessment and Data Mining
The process of extracting meaningful information from very large databases. One of the main steps of the KDD process.
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Using Business Analytics in Franchise Organizations
Analytical techniques used to find out the hidden relationships or patterns residing in the organizational data.
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Studying Individualized Transit Indicators Using a New Low-Cost Information System
Interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets
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Data Mining and the KDD Process
The process of extraction of implicit, previously unknown, and potentially useful knowledge from data. It uses Machine Learning, statistical and visualization techniques to discover and present knowledge in a form that is easily comprehensible to humans. It is a phase in a bigger process: the Knowledge Discovery in Databases (KDD) process.
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Decision-Making Approaches for Airport Surrounding Traffic Management
A process of extracting information and recognizing patterns in large datasets by combining statistical learning and database management.
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Web-Based Data Collection for Educational Research
A method for analyzing a huge amount of data in order to find out patterns and abnormalities of the data observed on the Internet.
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Semantic Methods for Data Mining in Smart Spaces
The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
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Quality Control Using Agent Based Framework
An interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
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SHARE: A European Healthgrid Roadmap
Automatically searching large volumes of data for patterns or associations.
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Academic Analytics
The process of using sophisticated data search capabilities and statistical algorithms to discover patterns in large data sets. Data mining techniques commonly used in academic analytics include regression analysis, rule induction, clustering, neural networks, and decision trees.
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Plan and Rules for Data Analysis Success: A Roadmap
This practice consists of extracting information from data as the objective of drawing knowledge from large quantities of data through automatic or semi-automatic methods. Data mining uses algorithms drawn from disciplines as diverse as statistics, artificial intelligence, and computer science in order to develop models from data; that is, in order to find interesting structures or recurrent themes according to criteria determined beforehand and to extract the largest possible amount of knowledge useful to companies. It groups together all technologies capable of analyzing database information in order to find useful information and possible significant and useful relationships within the data.
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Default Probability Prediction of Credit Applicants Using a New Fuzzy KNN Method with Optimal Weights
Extracting useful knowledge from a huge data using analytical techniques such as artificial intelligence techniques, neural networks, and advanced statistical tools.
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Big Data and Web Intelligence for Condition Monitoring: A Case Study on Wind Turbines
A subfield of computer science focused on patterns discovery in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.
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Decision Support Systems for Cardiovascular Diseases Based on Data Mining and Fuzzy Modelling
The process of extracting previously unknown and potentially useful knowledge, hidden in large volumes of data.
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Application of Data Mining Techniques for Breast Cancer Prognosis
Is the process of analyzing large amount of data in order to discover hidden patterns and relationships.
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Improving Knowledge Availability of Forensic Intelligence through Forensic Pattern Warehouse (FPW)
The practice of analyzing, examining data from different perspectives and summarizing to generate new information.
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Analyzing Process Data from Technology-Rich Tasks
The process of searching for patterns in data without a theoretical framework.
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Similarity Retrieval and Cluster Analysis Using R* Trees
As a key step in the knowledge discovery from data (KDD) process, it is intended to extract interesting (nontrivial, implicit, previously unknown, and potentially useful) patterns. Inference: The act or process of deriving a conclusion from stored data or known facts. While cognitive psychology studies human inference, automated inference algorithms have been studied in artificial intelligence and in its subfield machine learning.
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Application of Blockchain Technology for Distributed Management of Digital Research Library Holdings
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Neural Network-Based Visual Data Mining for Cancer Data
Nontrivial extraction of implicit, previously unknown and potentially useful information from data. Typically, analytical methods and tools are applied to data with the aim of identifying patterns, relationships or obtaining databases for tasks such as classification, prediction, estimation or clustering
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A Mapping of Knowledge Management Techniques and Tools for Sustainable Growth in the Public Sector
The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records.
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Index and Materialized View Selection in Data Warehouses
The nontrivial extraction of implicit, previously unknown, and potentially useful information from data.
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Understanding Data Analytics Is Good but Knowing How to Use It Is Better!
This practice consists of extracting information from data as the objective of drawing knowledge from large quantities of data through automatic or semi-automatic methods. Data mining uses algorithms drawn from disciplines as diverse as statistics, artificial intelligence, and computer science in order to develop models from data; that is, in order to find interesting structures or recurrent themes according to criteria determined beforehand and to extract the largest possible amount of knowledge useful to companies. It groups together all technologies capable of analyzing database information in order to find useful information and possible significant and useful relationships within the data.
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Data Mining in Public Administration
This is a process of sifting through the mass of organizational data to identify patterns critical for decision support.
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Algorithms for Association Rule Mining
Extraction of interesting, non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases.
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Feature Selection Algorithm Using Relative Odds for Data Mining Classification
The process of extracting hidden but useful information from data sources.
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Using the Text Categorization Framework for Protein Classification
The application of analytical methods and tools to data for the purpose of identifying patterns and relationships such as classification, prediction, estimation, or affinity grouping.
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A Process-Oriented Framework for Regulating Artificial Intelligence Systems
Is a process of discovering various models, summaries, and derived values, knowledge from a large database. Another definition is that it is the process of using statistical, mathematical, logical, AI methods and tools to extract useful information from large databases.
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Intelligent Recommender Systems in E-Commerce: Opportunities and Challenges for Online Customers
An interdisciplinary field that combines computer science and statistics with an overall goal to discover patterns in very large data sets and later turn them to valuable information that can be used by decision makers.
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Towards an Effective Imaging-Based Decision Support System for Skin Cancer
Process involved on the harvesting of information from unprocessed data.
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Social Media Marketing: Web X.0 of Opportunities
Data mining is a technologically driven process of using algorithms to analyze data from multiple perspectives and extract meaningful patterns that can be used to predict future users behavior The market basket analysis system that Amazon.com uses to recommend new products to its customers on the basis of their past purchases is a widely known example of how data mining can be utilized in marketing.
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Big Data Mining Algorithms
Data mining is widely used in fields such as science, engineering, medicine, and business.
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Growing Self-Organizing Maps for Data Analysis
The application of analytical methods and tools to data for the purpose of identifying patterns, relationships or obtaining systems that perform useful tasks such as classification, prediction, estimation, or affinity grouping.
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Regression-Based Recovery Time Predictions in Business Continuity Management: A Public College Case Study
The data-based knowledge discovery phase throughout which data are efficiently processed for thorough analysis and formulation of rules – based decisions and predictions regarding specific facts.
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Big Data Visualization of Association Rules and Frequent Patterns
non-trivial extraction of implicit, previously unknown, and potentially useful information from data.
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Unleashing the Potential of Every Child: The Transformative Role of Artificial Intelligence in Personalized Learning
The computational process of extracting insights from large sets of data by discovering meaningful patterns and correlations.
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Classification and Recommendation With Data Streams
Is the process that identifies and collects patterns in data sets.
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ACO_NB-Based Hybrid Prediction Model for Medical Disease Diagnosis
It is a technique used to turn raw data into some useful information. By using software to look for patterns in large batches of data, companies can learn more about their customers to develop more effective marketing strategies, in order to increase sales and decrease costs.
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Assessment in Academic Libraries
Analysis of large volumes of data from systems or repositories for the purpose of identifying patterns or relationships, ultimately facilitating decision-making.
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Leveraging Applications of Data Mining in Healthcare Using Big Data Analytics: An Overview
Data Mining is the process of extracting hidden, implicit, novel, and useful information from large volume of data. It has emerged as a unique combination of several fields of science and technology including statistics, database systems, computer programming, machine learning, and artificial intelligence. Data mining spans a wide range of applications in medicine and population health (study of drug implications, disease outbreak), bioinformatics (protein interactions, gene sequence analysis), engineering (intrusion detection and network security, flow classification, Web mining), business (fraud detection, decision support systems, risk analysis, forecasting market trend), and environmental studies (flood prediction).
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First of All, Understand Data Analytics Context and Changes
This practice consists of extracting information from data as the objective of drawing knowledge from large quantities of data through automatic or semi-automatic methods. Data mining uses algorithms drawn from disciplines as diverse as statistics, artificial intelligence, and computer science in order to develop models from data; that is, in order to find interesting structures or recurrent themes according to criteria determined beforehand and to extract the largest possible amount of knowledge useful to companies. It groups together all technologies capable of analyzing database information in order to find useful information and possible significant and useful relationships within the data.
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Open Source Software Usage in Education and Research: Network Traffic Analysis as an Example
A computer process that discovers hidden information or relations in a large amount of data, using artificial intelligence and machine learning methods.
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Hybrid Computational Intelligence
An interdisciplinary subfield of computer science that corresponds to the computational process of discovering patterns in large data collections.
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Decision Tree Applications for Data Modelling
Also known as knowledge discovery in database (KDD), which is a process of knowledge discovery by analysing data and extracting information from a dataset using machine learning techniques
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Information Science in the Analytics of Healthcare Data
Process of analysing large databsets in order to generate new information.
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The Stakes of Social Media: Analyzing User Sentiments
Family of tools allowing the analysis of a large amount of data on social media.
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Identifying Batch Processing Features in Workflows
Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It is a science of extracting useful information from large data sets or databases.
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Expression and Processing of Inductive Queries
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Bibliomining for Library Decision-Making
The extraction of non-trivial and actionable patterns from large amounts of data using statistical and artificial intelligence techniques. Directed data mining starts with a question or area of interest, and patterns are sought that answer those needs. Undirected data mining is the use of these tools to explore a dataset for patterns without a guiding research question.
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Toward the 4th Agenda 2030 Goal: AI Support to Executive Functions for Inclusions
The process of analyzing datasets in order to discover new patterns that might improve the model.
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Incorporating Fuzzy Logic in Data Mining Tasks
The core of the KDD process, involving the inferring of algorithms that explore the data, develop the model, and discover previously unknown patterns
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A Survey on Forensic Accounting
Data mining refers to as data treatment by means of advanced data inspection competence and statistical procedures to ascertain patterns and associations in an already existing large database to uncover novel significance in data.
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Integration of Clinical and Genomic Data for Decision Support in Cancer
The analysis of observational datasets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner.
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Computer Technologies in Logic Education
Knowledge discovery methodology based on artificial intelligence techniques.
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Introduction to Data Mining
Technique for extracting hidden knowledge from data.
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Application of Fuzzy Logic to Fraud Detection
Using powerful data collection methods to analyze a company’s database or data stores and select information that supports a specific objective.
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Data Mining in Franchising
Analytical techniques used to find out the hidden relationships or patterns residing in the organizational data.
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Fluctuation of Emotion in Stress and Different Coping Mechanisms During the COVID-19 Pandemic
In the digital age, data mining unearths invaluable insights, helping us navigate the sea of information and make informed decisions.
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Artificial Intelligence a Driver for Digital Transformation
The process of analyzing a large volume of data and bring out models, correlations and trends, in order to identify recurring patterns while establishing problem-solving relationships.
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Business Intelligence Applied to Tourism
Uses a set of techniques from Statistics, Machine Learning, Pattern Recognition, and Database Management Systems that make it possible to explore a collection of data to detect patterns and discover knowledge in those data.
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Opportunities and Challenges of Big Data in Healthcare
The process of extracting new, useful, understandable and previously unknown knowledge from information that might help in decision making.
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Integrated Data Architecture for Business
The computational process and technique to find and discover patterns from large data sets.
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Teaching and Using Analytics in Management Education
A process for discovering patterns in large data bases using a variety of analytical techniques including artificial intelligence.
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An Innovative Solar Energy and Decision Support System Approach by Using IoT
The method of investigating and identifying patterns, trends, correlations and establishing relationships in large databases to derive useful information from raw data and to forecast future outcomes. Data mining is the analysis step of the “knowledge discovery in databases” process and it can be used for Business Intelligence.
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A Survey on Recent Recommendation Systems for the Tourism Industry
Data mining is the process of extracting useful knowledge from data or big data, for example, information stockrooms, for productive examination, information mining calculations, and encouraging business.
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Investigating the Factors for Predictive Marketing Implementation in Algerian Organizations
This practice consists of extracting information from data as the objective of drawing knowledge from large quantities of data through automatic or semi-automatic methods. Data mining uses algorithms drawn from disciplines as diverse as statistics, artificial intelligence, and computer science in order to develop models from data; that is, in order to find interesting structures or recurrent themes according to criteria determined beforehand, and to extract the largest possible amount of knowledge useful to companies. It groups together all technologies capable of analyzing database information in order to find useful information and possible significant and useful relationships within the data.
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Using the Flipped Classroom to Improve Knowledge Creation of Master's-Level Students in Engineering
Data Mining refers to extracting or mining knowledge from large amounts of data. Data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing, and data visualization. Data mining is used to uncover hidden patterns in the underlying data which can be used for decision making process.
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Supply Chain Analytics: Challenges and Opportunities
The actual extraction of knowledge from data via technologies that incorporate various quantitative and qualitative methods.
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Integrated Data Architecture for Business
The computational process and technique to find and discover patterns from large data sets.
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Untangling BioOntologies for Mining Biomedical Information
The process of discovering meaningful correlations, patterns, and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques.
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Data Mining of Chemogenomics Data Using Activity Landscape and Partial Least Squares
A technique of data analysis such as statistics, pattern recognition, the artificial intelligence to a large quantity of data for getting knowledge.
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Quality Improvement of Healthcare Services Through Data Analytics Processes
Data mining refers to the process of discovering patterns, trends, and relationships within datasets using the techniques and algorithms in Supervised and Unsupervised Learning.
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Application of Utility Mining in Supply Chain Management
It is the process of analyzing large amounts of data in order to discover patterns and other information.
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Incremental Discovery of Fuzzy Functional Dependencies
Also called knowledge discovery in databases (KDD), it is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data.
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A Data Mining Algorithm for Accessing Research Literature in Electronic Databases: Boolean Operators
Data mining is the process of seeking patterns in large databases and extracting relevant information using machine learning or algorithmic and database techniques.
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Privacy-Preserving Data Mining
The process of automatically searching large volumes of data for patterns.
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IoT and Its Real-Time Application in Agriculture
It is the process of mining the patterns in a large dataset with the help of statistics and logistics.
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Data Mining Applications in Computer-Supported Collaborative Learning
Is a process of analyzing data from different perspectives and summarizing it into useful information. The information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
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Multimedia Data Mining Trends and Challenges
An intelligent and automatic process of identifying and discovering useful structures such as patterns, models, and relations in data.
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Social Network Analysis: Basic Concepts, Tools, and Applications
Is the process of analyzing for knowledge discovery in databases to identify patterns and gain knowledge from large data sets.
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Data Warehouse and Business Intelligence Systems in the Context of E-HRM
Subsumes a variety of methods to extract unknown patterns out of a large amount of data. Data mining methods originate from the area of machine learning, statistics, and artificial intelligence. The main tasks of data mining are classification, segmentation, and association analysis.
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Exploiting Captions for Multimedia Data Mining
Searching for insights in large quantities of data.
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A New Approach to Reducing Social Engineering Impact
A set of techniques that analyze data for the sake of finding patterns and relationships.
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Integrated Data Mining and Business Intelligence
Data mining is the process of analyzing data from different perspectives and summarizing it into useful and actionable information. Data mining software is one of a number of analytical tools for analyzing data.
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Parallel, Distributed, and Grid-Based Data Mining: Algorithms, Systems, and Applications
A subset process of knowledge discovery. It is concerned with the application of mining algorithms to data.
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Analyzing and Predicting Student Academic Achievement Using Data Mining Techniques
Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data using methods at the intersection of artificial intelligence, machine learning, statistics and database management systems.
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