What is Clustering

1.

Algorithm technique that allows machines to group similar data into larger data categories. Learn more in: Artificial Intelligence a Driver for Digital Transformation

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Organization of data in some semantically meaningful way such that each cluster contains related data while the unrelated data are assigned to different clusters. The clusters may not be predefined. Learn more in: Data Mining Tools: Association Rules

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The term clustering refers to the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters. Learn more in: Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence Framework

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Unsupervised grouping of data. Learn more in: E-Government Documents and Data Clustering

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An unsupervised technique for grouping the dataset into classes of similar data. Learn more in: Data Clustering Using Sine Cosine Algorithm: Data Clustering Using SCA

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The action of grouping together of patterns into dissimilar clusters with respect to a similarity measure. Learn more in: Improved Wavelet Neural Networks and Its Applications in Function Approximation

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Clustering is an unsupervised learning technique wherein the intragroup similarity is the maximum, and inter-group similarity is the minimum. Learn more in: Deep-Auto Encoders for Detecting Credit Card Fraud

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Grouping technique according to distance or similarity. Learn more in: Implementation of an Intelligent Model Based on Machine Learning in the Application of Macro-Ergonomic Methods in a Human Resources Process Based on ISO 12207

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Clustering is an unsupervised learning problem and collection of objects in such a way that similar objects in the same group and dissimilar objects are in other groups. Learn more in: Balanced Energy Consumption Approach Based on Ant Colony in Wireless Sensor Networks

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Assigning similar elements to one group, which increases intra-cluster similarity and decreases inter-cluster similarity. Learn more in: Cancer Biomarker Assessment Using Evolutionary Rough Multi-Objective Optimization Algorithm

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Technique that consists of conceptually devising meaningful groups of objects that share some common characteristics. This involves dividing the objects into groups and performing classification that is assigning individual objects to these groups. Learn more in: Big Data in Massive Parallel Processing: A Multi-Core Processors Perspective

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It is the process of partitioning a set of data or objects into a set of meaningful subclasses, called clusters. Learn more in: A Novel Method for Calculating Customer Reviews Ratings

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The process of identifying values that are similar by one or more criteria. Learn more in: A Data Warehouse Integration Methodology in Support of Collaborating SMEs

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The process of assigning individual data items into groups (called clusters) so that items from the same cluster are more similar to each other than items from different clusters. Often similarity is assessed according to a distance measure. Learn more in: Clustering and Visualization of Multivariate Time Series

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A process of partitioning the given data points into homogeneous groups called clusters. Learn more in: KD-Tree Based Clustering for Gene Expression Data

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Analysis of large set of data to provide a finite set of categories to describe similarities among its objects. Learn more in: Clustering Global Entrepreneurship through Data Mining Technique

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A partition of observations into groups called clusters whose members have similar properties. Learn more in: Principal Component Analysis of Hydrological Data

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Grouping the nodes of an ad hoc network such that each group is a self-organized entity having a cluster-head which is responsible for formation and management of its cluster. Learn more in: Evolutionary Computing Approach for Ad-Hoc Networks

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Clustering is a classification technique where similar kinds of objects are grouped together. The similarity between the objects maybe determined in different ways depending upon the use case. Therefore, clustering in measurement space may be an indicator of similarity of image regions, and may be used for segmentation purposes. Learn more in: Improved Lymphocyte Image Segmentation Using Near Sets for ALL Detection

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Clustering is an analysis method for grouping of probes and samples by similarity. Similar data sets fall into the same cluster while dissimliar data sets fall into different clusters. For the hierarchical clustering a hierarchy of clusters is determined. Thus, one large cluster comprising all data sets is stepwise subdivided into smaller clusters down to singletons which are clusters containing a single data set. Learn more in: Gene Expression Profiling with the BeadArrayTM Platform

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The assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. Learn more in: Modules in Biological Networks: Identification and Application

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An allocation or mapping of data according to the various criterion. Learn more in: A Novel Approach to Segmentation Using Customer Locations Data and Intelligent Techniques

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It’s a process in which a group of objects which are similar to each other are grouped together. Learn more in: Recent Trends in Spatial Data Mining and Its Challenges

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Aims to group data automatically according to their degree of similarity. Learn more in: Big Data Analytics for Intrusion Detection: An Overview

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A technique in data mining that attempts to identify the natural groupings of data, such as income groups to which customers belong Learn more in: Exploiting the Strategic Potential of Data Mining

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An unsupervised learning method different from classification. Large databases are separated into the form of small different subgroups or clusters. Methods of clustering are Partitioned Clustering, Hierarchical Clustering and Density Base Clustering. Learn more in: Health Information System

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Clustering or cluster analysis is a set of techniques of multivariate data analysis aimed at selecting and grouping homogeneous elements in a data set. Clustering techniques are based on measures relating to the similarity between the elements. In many approaches this similarity, or better, dissimilarity, is designed in terms of distance in a multidimensional space. Clustering algorithms group items on the basis of their mutual distance, and then the belonging to a set or not depends on how the element under consideration is distant from the collection itself. Learn more in: Building Gene Networks by Analyzing Gene Expression Profiles

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It is a categorization process in which the data are grouped based on the features. Learn more in: A Study on Supervised Machine Learning Technique to Detect Anomalies in Networks

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Clustering is a techniques to segment the items which they have similiar values or attributes. Learn more in: Understanding Customer Behavior through Collaboration RFM Analysis and Data Mining Using Health Life Center Data

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Clustering uses attributes to group similar objects or items together to succinctly represent a collection of objects or items. Learn more in: Statistical Methods for Conducting the Ontology and Classifications of Fake News on Social Media

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Form of data analysis that groups observations to clusters. Similar observations are grouped in the same cluster, whereas dissimilar observations are grouped in different clusters. As opposed to classification, there is not a class attribute and no predefined classes exist. Learn more in: Composite Classifiers for Bankruptcy Prediction

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Process of segregating a big group of elements into subgroups of elements with similar traits and characteristics. Learn more in: Quality Systems for a Responsible Management in the University: Measuring the Performance of Teaching Staff

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Clustering is a procedure to group the different objects into the same category which has similar properties measured by the specific metrics. Learn more in: Use of PCA Solution in Hierarchy and Case of Two Classes of Data Sets to Initialize Parameters for Clustering Function: An Estimation Method for Clustering Function in Classification Application

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Clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters) it is unsupervised way. Learn more in: Ideating a Recommender System for Business Growth Using Profit Pattern Mining and Uncertainty Theory

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An unsupervised data mining technique used to find either useful or meaningful patterns and groupings within a dataset. Learn more in: Cluster Analysis in R With Big Data Applications

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It is identifying elements with similar characteristics, and grouping cases with similar characteristics together. Learn more in: Applying Fuzzy Data Mining to Tourism Area

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It is defined as a group of similar elements. Learn more in: An Approach for Estimating the Opportunity Cost Using Temporal Association Rule Mining and Clustering

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Statistical grouping of the phenomenon under study in different clusters or classes, based on member similarity within each cluster and dissimilarity of members across clusters. Learn more in: Studying Educational Digital Library Users and the Emerging Research Methodologies

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Clustering can simply be referred to as a 'Grouping' based on numerous or similar properties. Learn more in: Descriptive Data Analytics on Dinesafe Data for Food Assessment and Evaluation Using R Programming Language: A Case Study on Toronto's Dinesafe Inspection and Disclosure System

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The process of discovering naturally-occurring groups of data elements. Learn more in: Featureless Data Clustering

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The unsupervised grouping of data items in the absence of class labels. Learn more in: Computer Aided Knowledge Discovery in Biomedicine

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Clustering is the task of grouping a set of objects in such a way that similarity of objects in the same group are compared to another group and discover that object in which group are more similar to each other than to those in other groups. Learn more in: Big Data Analytics: Educational Data Classification Using Hadoop-Inspired MapReduce Framework

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Clustering is a type of unsupervised learning technique where data objects are placed into different collections called clusters, based on their degree of dissimilarity. All like data objects are a part of the same cluster. Learn more in: Machine Learning and Its Application in Monitoring Diabetes Mellitus

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Automatic aggregation of data in classes according to a given distance (usually Euclidean). It is supervised if a subset of data is used in order to learn the classification embedded rule to be applied to the rest of the data; otherwise unsupervised. Learn more in: ALBA Cooperative Environment for Scientific Experiments

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Grouping similar kind of data elements. It is used to discover similar patterns from a sea of data. The similarity between the objects in the same cluster is greater than that of the different clusters. Learn more in: Extraction of Protein Sequence Motif Information using Bio-Inspired Computing

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for the aims of this work, we deliberately focus on one definition of clustering, that is the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait – often proximity according to some defined distance measure. Learn more in: FOL Learning for Knowledge Discovery in Documents

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The clustering consists in partitioning a set of n objects in k = 2 non-empty subsets (called clusters) in such a way that the objects in any cluster have similar attributes and, at the same time, are different from the objects in any other cluster. Learn more in: Improving the K-Means Clustering Algorithm Oriented to Big Data Environments

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Organization of data in some semantically meaningful way such that each cluster contains related data while the unrelated data are assigned to different clusters. The clusters may not be predefined. Learn more in: Data Mining Tools: Formal Concept Analysis and Rough Sets

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is the process of organizing objects into groupings (clusters) where members of one group are similar to each other but dissimilar to the objects belonging to other groups. In the field of machine learning it is assigned under the category of unsupervised learning as we have to find structure in unlabelled data. Learn more in: Data Integration for Regulatory Gene Module Discovery

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It is a technique that classifies instances into classes by calculating the distance between them and other instances. The instances that have the least distance between them are grouped into the same class. It is a type of unsupervised learning. Learn more in: Data Mining in Tourism

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A process of grouping instances into clusters so that instances are similar to one another within a cluster but dissimilar to instances in other clusters. Learn more in: Knowledge Discovery from Genomics Microarrays

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Partition of a set of data in groups (clusters) in such a way that each datum is maximally close in some defined metric to all the others belonging to the same cluster and maximally far from every other datum belonging to different clusters. Learn more in: Information Technology in Brain Intensive Therapy

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A group of mathematical methods to divide the objects (such as customers) into different groups. Could be a technique to perform market segmentation. Learn more in: Clustering Tourists Based on Reason for Destination Choice: Case of Izmir

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Assigning similar elements to one group, which increases intra-cluster similarity and decreases inter-cluster similarity. Learn more in: Cancer Gene Expression Data Analysis Using Rough Based Symmetrical Clustering

55.

The term clustering refers to the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters. Learn more in: Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence Framework

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It is a working model that brings together the companies and the supporting firms and institutions operating in the same or similar line of business, geographically close to each other, cooperating and competing with each other. Learn more in: Strategic Management in SMEs in the Context of Clustering

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An Unsupervised Learning technique of Data Mining used for identification and grouping of objects with similar characteristics. Learn more in: A Hybrid Intelligent Risk Identification Model for Configuration Management in Aerospace Systems

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The process of organizing objects into groups whose members are similar in some way. Learn more in: Big Data Analytics in Retail Supply Chain

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The process of associating and grouping very similar (but not identical) profiles that reveals patterns, trends, requirements, characteristics, relationships, and structures. Learn more in: Transformation Framework for Supply Chain Segmentation in Digital Business

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Inductive task where a set of unlabeled objects is partitioned into groups (clusters) and where objects in a same cluster have similar characteristics, maximizing the similarity intra cluster and minimizing the similarity inter cluster. Learn more in: Methods and Techniques of Data Mining

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Division of data into groups of similar objects is called Clustering. Certain fine details are lost by representing the data by fewer clusters but it achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical analysis. According to machine learning perspective, clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. From a practical perspective clustering plays an important role in data mining applications such as scientific data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others. Learn more in: Secure Data Analysis in Clusters (Iris Database)

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Clustering is also called unsupervised learning and is a powerful technique for pattern classification. It is a process to group, based on some defined criteria, two or more terms together to form a large collection. In the context of image segmentation, it is often considered as the multi-dimensional extension of the thresholding technique. Learn more in: Half Century for Image Segmentation

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It is a technique to find out hidden patterns and knowledge from a given set of patterns and data. Learn more in: Application of Charge System Search Algorithm for Data Clustering

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The process of organizing objects into groups whose members are similar in some way. A cluster is therefore a collection of objects, which are “similar” between them and are “dissimilar” to the objects belonging to other clusters. Learn more in: Nature-Inspired Cooperative Strategies for Optimization

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It is the task of decomposing or partitioning a dataset into groups so that the points in one group are similar to each other and are as different as possible from the points in the other groups. Learn more in: Image Processing and Machine Learning Techniques for the Segmentation of cDNA

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A way to form clusters of patterns without a-priori knowledge. Learn more in: Parallel, Distributed, and Grid-Based Data Mining: Algorithms, Systems, and Applications

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Technique of grouping similar objects used mainly to deal with scalability, but its aim depends on the context it is applied in. Learn more in: Clustering and 5G-Enabled Smart Cities: A Survey of Clustering Schemes in VANETs

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Clustering is the process where given a set of objects and a similarity or distance function between pairs of them, we can partition these objects into groups such that similar objects are grouped together while dissimilar are under different groups, The groups are called clusters. Learn more in: Efficient Algorithms for Clustering Data and Text Streams

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Clustering is the classification of objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance measure. Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Learn more in: The Clustering of Large Scale E-Learning Resources

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It is a process in which the network is divided into logical groups called clusters. Each cluster has a cluster head and the remaining members are called cluster members. The cluster heads communicate with each other via cluster gateway nodes. Learn more in: Genetic-Algorithm-Based Optimization of Clustering in Mobile Ad Hoc Network

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It is used to discover groups of similar items. Learn more in: A Survey on Recent Recommendation Systems for the Tourism Industry

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Clustering is a grouping of a particular set of objects based on their characteristic, that is, aggregating them according to their similarities. Regarding to data mining, this methodology partitions the data implementing a specific join algorithm, most suitable for the desired information analysis. Learn more in: Dynamic Quota Calculation System (DQCS): Pricing and Quota Allocation of Telecom Customers via Data Mining Approaches

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A set of techniques of multivariate data analysis aimed at selecting and grouping homogeneous elements in a data set. Clustering techniques are based on measures relating to the similarity between the elements. In many approaches this similarity, or better, dissimilarity, is designed in terms of distance in a multidimensional space. Clustering algorithms group items on the basis of their mutual distance, and then the belonging to a set or not depends on how the element under consideration is distant from the collection itself. Learn more in: Clustering Techniques for Revealing Gene Expression Patterns

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In data mining Clustering is a technique used to partition a set of data elements into sub-classes or cluster. A cluster is a collection of objects having some similarity. Some of the clustering techniques are: k-means clustering and expectation maximization (EM) clustering. For more information on Clustering, see Appendix. Learn more in: Data Security Issues and Solutions in Cloud Computing

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An organizational unit of the wireless sensor network, and it is a mean to achieve hierarchical routing in wireless sensor network. It comprises grouping nodes into clusters each cluster has a head for coordinating its cluster members' work and receiving their data, aggregating it, and sending it outside the cluster. Learn more in: Clustering in Wireless Sensor Network: A Study on Three Well-Known Clustering Protocols

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Clustering is the technique of grouping similar data samples based on distance and similarity criterion. Clustering is a statistical data analysis task which has been employed in many areas such as information retrieval, data mining, bioinformatics, data compression, etc. Learn more in: Analysis of Gravitation-Based Optimization Algorithms for Clustering and Classification

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Clustering is an algorithmic concept where data points occur in bunches, rather than evenly spaced over their range. A data set which tends to bunch only in the middle is said to possess centrality. Data sets which bunch in several places do not possess centrality. What they do possess has not been very much studied, and there are no infallible methods for locating the describing more than one cluster in a data set (the problem is much worse when some of the clusters overlap). Learn more in: Clustering Algorithms for Data Streams

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Assigning similar elements to one group, which increases intra-cluster similarity and decreases inter-cluster similarity. Learn more in: Remote Sensing Image Classification Using Fuzzy-PSO Hybrid Approach

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To group items based on geographic properties or similarities. Learn more in: Multi-Criteria Decision Making With Machine Learning for Vehicle Routing Problem

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Clustering is also called unsupervised learning and is a powerful technique for pattern classification. It is a process to group, based on some defined criteria, two or more terms together to form a large collection. In the context of image segmentation, it is often considered as the multi-dimensional extension of the thresholding technique. Learn more in: The Understanding of Spatial-Temporal Behaviors

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Clustering is the division of data into groups of similar objects. The goal is to discover the “natural” grouping(s) of a set of patterns, points, or objects. Learn more in: Examining the Evolution of E-Government Development of Nations Through Machine Learning Techniques

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The task of grouping a set of objects into a group (based on some attribute), in such a way that they are more similar within them than to those in other groups. Learn more in: Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application

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Grouping different data based on the similarity. Learn more in: Information Science in the Analytics of Healthcare Data

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Clustering is a unsupervised learning technique used to group the data based on similarity of the data. The class/target of the data is not available and the algorithm itself will group the data into classes or groups. Learn more in: Medical Image Classification

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Data mining technique to partition data objects into a set of groups such that the intra group similarity is maximized and inter group similarity is minimized. Learn more in: Association Rule Mining

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Objects are being grouped based on similarity. Each cluster contains objects which are more similar among each other than to objects in other clusters. Learn more in: Text Mining

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A group of similar elements. Learn more in: Optimal Ordering Policy With Inventory Classification Using Data Mining Techniques

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Process that separates objects in groups (clusters) evaluating the similarity between them. The goal is to put similar objects inside the same cluster and dissimilar ones in different clusters. The number of initial clusters may not be known. Learn more in: Concept-Based Text Mining

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The process of grouping data instances into subsets in such a manner that similar instances are grouped together into the same cluster, while different instances belong to different clusters Learn more in: Incorporating Fuzzy Logic in Data Mining Tasks

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Type of machine learning that does not require training data or supervision. The k -means algorithm, nonnegative matrix factorization (NMF), and latent Dirichlet allocation (LDA) are examples of clustering algorithms. This is also referred to as unsupervised machine learning . Learn more in: Applying Bibliometrics to Examine Research Output and Highlight Collaboration

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An unsupervised machine learning technique capable to automatically partition a set of items, described by a set of features, into disjoint groups, clusters. Also known as unsupervised learning mechanism, data mining technique. Learn more in: User Driven Query Framework of Social Networks for Geo-Temporal Analysis of Events of Interest

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Can be considered the most important unsupervised learning problem of organizing objects into groups whose members are similar in some way. A cluster is therefore a collection of objects that are similar between them and are dissimilar to the objects belonging to other clusters. Learn more in: Biometric Identification Techniques

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Clustering is an unsupervised learning method used for the abstraction of unlabeled data into classes or groups based on their similarity. Learn more in: Fog-Cloud Collaboration for Real-Time Streaming Applications: FCC for RTSAs

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Automated process for grouping similar data together. It minimizes the intra-cluster distance while maximizing the inter-cluster distance. It is a multi-objective optimization, some instances are NP hard (when the number of classes is higher than two). Learn more in: Ant Colonies and Data Mining

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A networking technique in which nodes in the network group themselves according to some network attributes to form hierarchical architectures. Learn more in: Quality of Service in Mobile Ad Hoc Networks

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Clustering is also called unsupervised learning and is a powerful technique for pattern classification. It is a process to group, based on some defined criteria, two or more terms together to form a large collection In the context of image segmentation, it is often considered as the multi-dimensional extension of the thresholding technique. Learn more in: Image Segmentation in the Last 40 Years

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Inductive task where a set of unlabeled objects is partitioned into groups (clusters) and where objects in a same cluster have similar characteristics, maximizing the similarity intra cluster and minimizing the similarity inter cluster. Learn more in: Data Mining and the KDD Process

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A mechanism to classify data, information, and/or knowledge according to various similarity measures. Learn more in: Crime Profiling System

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Clustering is an unsupervised learning technique that groups a set of objects into clusters based on similarity. Learn more in: Machine Learning Techniques for IoT-Based Indoor Tracking and Localization

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Grouping of objects according to some pre-defined criteria based on some algorithm techniques is known as clustering. Learn more in: Swarm Intelligence in Solving Bio-Inspired Computing Problems: Reviews, Perspectives, and Challenges

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A closely related term to classification. However, unlike classification whose probable data sets are known prior to the actual execution, clustering is blind and learns from the provided data sets without any knowledge; hence, training is unsupervised. Learn more in: Quality and Effectiveness of ERP Software: Data Mining Perspective

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A process to group, based on some defined criteria, two or more terms together to form a large collection. In the context of image segmentation, clustering is to gather several pixels or groups of pixels with similar property to form a region. Learn more in: Recent Progress in Image and Video Segmentation for CBVIR

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Grouping of objects according to some pre-defined criteria based on some algorithm techniques is known as clustering. Learn more in: A Comprehensive Review of Nature-Inspired Algorithms for Feature Selection

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Clustering is the process of process of dividing the sensor nodes into a number of clusters such that intra cluster similarity is higher and inter cluster is lower. There are various parameters such as residual energy, distance from the base station and node degree etc. to choose the cluster head. Clustering approaches are of great significance from the energy efficiency perspective. The major task of the cluster head is to perform the data collection from the cluster members and transfer it to the base station after performing data aggregation. Aggregation is usually done to reduce the data packets by performing certain operations such as minimum, maximum, average etc. Learn more in: Priority Encoding-Based Cluster Head Selection Approach in Wireless Sensor Networks

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Clustering is an unsupervised learning technique where the input is not tagged. Clustering identify patterns in the data based on datapoint similarities. Learn more in: Data Avalanche: Harnessing for Mobile Payment Fraud Detection Using Machine Learning

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Clustering is also called unsupervised learning and is a powerful technique for pattern classification. It is a process to group, based on some defined criteria, two or more terms together to form a large collection In the context of image segmentation, it is often considered as the multi-dimensional extension of the thresholding technique. Learn more in: A Review of Image Segmentation Evaluation in the 21st Century

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A process to divide a set of data into groups called clusters, where the elements inside a group have higher similarity to each other than the similarity between elements of different groups. Learn more in: An Uncertainty-Based Model for Optimized Multi-Label Classification

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It is the process of dividing a dataset into groups of similar data items. Learn more in: Data Clustering Algorithms Using Rough Sets