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What is Outliers

Chapter 4
Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities
Are values that stays in an abnormal distance from other values in a random sample from a population.
Published in Chapter:
Predicting the Academic Performance of Students Using Utility-Based Data Mining
Sidath R. Liyanage (University of Kelaniya, Sri Lanka) and K. T. Sanvitha Kasthuriarachchi (University of Kelaniya, Sri Lanka)
Copyright: © 2020 |Pages: 30
DOI: 10.4018/978-1-7998-0010-1.ch004
Abstract
Data mining in education has become an important topic in the sphere of influence of data mining. Mining educational data encompasses developing models, plotting data, and utilizing machine learning algorithms to derive patterns on educational data by attempting to uncover hidden patterns, create information for hidden relationships using educational statistics, and perform many more operations that are unfeasible using traditional computational tools. This research aims to identify the main factors that influence the academic performance of learners in tertiary education system in Sri Lanka. A conceptual framework and an analytical framework on factors affecting the academic performance was constructed with this aim. The analytical framework was then validated with the data collected from technology learners in a tertiary educational institute.
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Chapter 12
To Monitor and Detect Suspicious Transactions in a Financial Transaction System Through Database Forensic Audit and Rule-Based Outlier Detection Model
An outlier is said to be an observation that is distant from other observations. Outlier detection is very important concept in data mining for data analysis. The application of outlier detection mainly focuses on the idea of detecting the suspicious or outlier financial transactions.
Published in Chapter: To Monitor and Detect Suspicious Transactions in a Financial Transaction System Through Database Forensic Audit and Rule-Based Outlier Detection Model; From: Organizational Auditing and Assurance in the Digital Age
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Chapter 11
Lesson Engagement and Outlier Extraction Analytics Through Learning Log Classification
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Chapter 9
Big Data Analytics in Action: Examples
An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal. Before abnormal observations can be singled out, it is necessary to characterize normal observations.
Published in Chapter: Big Data Analytics in Action: Examples; From: Big Data Analytics for Entrepreneurial Success
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Chapter 11
Age in the Consumer Behavior Change: Evidence From Awareness, Perceived Value, and Use of Mobile Banking
These are the values that are quite distant from others in the regression model and may significantly influence its predictive capability.
Published in Chapter: Age in the Consumer Behavior Change: Evidence From Awareness, Perceived Value, and Use of Mobile Banking; From: Handbook of Research on Consumer Behavior Change and Data Analytics in the Socio-Digital Era
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Chapter 8
Plan and Rules for Data Analysis Success: A Roadmap
An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal. Before abnormal observations can be singled out, it is necessary to characterize normal observations.
Published in Chapter: Plan and Rules for Data Analysis Success: A Roadmap; From: Big Data Analytics for Entrepreneurial Success
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Chapter 9
A Comprehensive Study of Various Fuzzy C-Means Clustering Algorithms
Since the majority of clustering methods require a minimum number of data points to construct a cluster, outliers are the ungrouped data points. Even if the outliers form a cluster, it is quite isolated from other clusters.
Published in Chapter: A Comprehensive Study of Various Fuzzy C-Means Clustering Algorithms; From: AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management
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Chapter 18
Student Collaborative Learning Strategies: A Logistic Regression Analysis Approach
One of the data preparation techniques for checking whether there are data points that could have undue influence on the results of the study is to check for outliers in the dataset. An outlier is technically a data point outside of your distribution; so potentially influential because may have undo effect on distribution.
Published in Chapter: Student Collaborative Learning Strategies: A Logistic Regression Analysis Approach; From: Handbook of Research on Innovations in Non-Traditional Educational Practices
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