Cluster Analysis

Number Of Volumes: 4
David Byrne
Emma Uprichard
SAGE Benchmarks in Social Research Methods
Cluster Analysis
September 2013 | 1584 pages | Sage UK
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Description

Cluster analysis is a family of techniques that sorts - or more accurately, classifies - cases into groups of similar cases. 'Data mining' encompasses a whole host of methodological procedures that are used for cluster analysis while 'classification' that is the analytical catalyst to the methodological approach. Thinking about issues of 'classification', 'cluster analysis' and 'data mining' together in this four-volume collection is appropriate, therefore, specifically with regards to developing a case based 'attitude' to quantitative analysis.

This collection does not simply focus on a set of methods, but in presenting a range of existing work together, the logic of what is arguably a methodological phase-shift in quantitative research is exposed. In effect, this four-volume collection sets forth an analytical strategy which is increasingly, both implicitly and explicitly, acknowledged across the disciplines as being rooted in the exploratory and descriptive investigation of cases.

Bringing work on classification, cluster analysis and data mining together in a way that is both accessible and timely with respect to the level of 'activity' going on in each of these related areas is important to signal a step-change in the kind of data analysis that is currently taking place, nationally and internationally, and to facilitate further research by demarcating the methodological research where the cutting edge approaches to data analysis lie.

Volume One: The Classics

Volume Two: (Useful) Key Texts

Volume Three: Cluster Analysis in Practice

Volume Four: Data Mining with Classification

Contents

VOLUME ONE: THE CLASSICS

  • Introduction
  • The Distinctiveness of Case-Oriented Research
  • The Causal Devolution
  • A Tradition of Natural Kinds
  • How "Natural" are "Kinds" of Sexual Orientation?'
  • The Logic of Classification
  • On the Logic of Classification
  • Scientific Classification
  • How things Work
  • How Real are Statistics? Four Possible Attitudes
  • EXTRACTS FROM The Growth of Cluster Analysis: Tryon, Ward, and Johnson
  • The Continuing Search for Order
  • Phenetic Taxonomy: Theory and Methods
  • Principles of Clustering
  • A Quantitative Approach to a Problem in Classification
  • Representation of Similarity Matrices by Trees
  • Data Clustering: A Review

VOLUME TWO: (USEFUL) KEY TEXTS

  • Introduction
  • Cluster Analysis in Perspective
  • The Practice of Cluster Analysis
  • A Review of Classification
  • Sociological Classification and Cluster Analysis
  • Cluster Analysis
  • Literature on Cluster-Analysis
  • Distance as a Measure of Taxonomic Similarity
  • Efficiency in Taxonomy
  • Numerical Taxonomy: Points of View
  • Hierarchical Grouping to Optimize an Objective Function
  • An Examination of Procedures for Determining the Number of Clusters in a Data Set
  • A Comparison of Some Methods of Cluster Analysis
  • A Nearest Centroid Technique for Evaluating the Minimum-variance Clustering Procedure
  • Measurement Problems in Cluster Analysis
  • Unresolved Problems in Cluster Analysis

VOLUME THREE: CLUSTER ANALYSIS IN PRACTICE

  • Introduction
  • The Use and Reporting of Cluster Analysis in Health Psychology: A Review
  • Cluster Analysis in Illness Perception Research: A Monte Carlo Study to Identify the Most Appropriate Method
  • The Psychiatric and Criminal Careers of Mentally Disordered Offenders Referred to a Custody Diversion Team in the United Kingdom
  • Fuzzy Cluster Analysis of Molecular Dynamics Trajectories
  • Mosaic: From an Area Classification System to Individual Classification
  • Creating the UK National Statistics 2001 Output Area Classification
  • Spatial Analysis Using Clustering Methods: Evaluating Central Point and Median Approaches
  • Use of Multiple Correspondence Analysis and Cluster Analysis to Study Dietary Behaviour: Food Consumption Questionnaire in the Su.Vi.Max. Cohort
  • Shopping-related Attitudes: a Factor and Cluster Analysis of Northern California Shoppers
  • Combining Cluster and Discriminant Analysis to Develop a Social Bond Topology of Runaway Youth
  • Heirarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method
  • Fuzzy Classification in Dynamic Environments
  • A Multistep Unsupervised Fuzzy Clustering Analysis of fMRI Time Series
  • A Note on K-modes Clustering
  • Using Self-Similarity to Cluster Large Data Sets
  • A Taxonomy of Similarity Mechanisms for Case-Based Reasoning
  • Using Case-based Approaches to Analyse Large Datasets: A Comparison of Ragin's fsQCA and Fuzzy Cluster Analysis
  • A Comparison of Cluster Analysis Techniques within a Sequential Validation Framework

VOLUME FOUR: DATA MINING WITH CLASSIFICATION

  • Introduction
  • Data Mining for Fun and Profit
  • Cluster Analysis using Data Mining Approach to Develop CRM Methodology to Assess the Customer Loyalty
  • Techniques of Cluster Algorithms in Data Mining
  • Data-Mining Discovery of Pattern and Process in Ecological Systems
  • Data Mining in Soft Computing Framework: A Survey
  • Data Mining and Internet Profiling: Emerging Regulatory and Technological Approaches
  • Statistical Classification Methods in Consumer Credit Scoring: A Review
  • Data Mining: An Overview from a Database Perspective
  • 50 Years of Data Mining and OR: Upcoming trends and Challenges
  • A General Framework for Mining Massive Data Streams
  • Confidence in Classification: A Bayesian Approach
  • Visualization Techniques for Mining Large Databases: A Comparison
  • Visualization of Fuzzy Clusters by Fuzzy Sammon Mapping Projection: Application to the Analysis of Phase Space Trajectories
  • Spatial-Temporal Data Mining Procedure: LASR
  • Turning Datamining into a Management Science Tool: New Algorithms and Empirical Results
  • Data Mining of Massive Datasets in Healthcare
  • Conclusion

Description

Cluster analysis is a family of techniques that sorts - or more accurately, classifies - cases into groups of similar cases. 'Data mining' encompasses a whole host of methodological procedures that are used for cluster analysis while 'classification' that is the analytical catalyst to the methodological approach. Thinking about issues of 'classification', 'cluster analysis' and 'data mining' together in this four-volume collection is appropriate, therefore, specifically with regards to developing a case based 'attitude' to quantitative analysis.

This collection does not simply focus on a set of methods, but in presenting a range of existing work together, the logic of what is arguably a methodological phase-shift in quantitative research is exposed. In effect, this four-volume collection sets forth an analytical strategy which is increasingly, both implicitly and explicitly, acknowledged across the disciplines as being rooted in the exploratory and descriptive investigation of cases.

Bringing work on classification, cluster analysis and data mining together in a way that is both accessible and timely with respect to the level of 'activity' going on in each of these related areas is important to signal a step-change in the kind of data analysis that is currently taking place, nationally and internationally, and to facilitate further research by demarcating the methodological research where the cutting edge approaches to data analysis lie.

Volume One: The Classics

Volume Two: (Useful) Key Texts

Volume Three: Cluster Analysis in Practice

Volume Four: Data Mining with Classification

Contents

VOLUME ONE: THE CLASSICS

  • Introduction
  • The Distinctiveness of Case-Oriented Research
  • The Causal Devolution
  • A Tradition of Natural Kinds
  • How "Natural" are "Kinds" of Sexual Orientation?'
  • The Logic of Classification
  • On the Logic of Classification
  • Scientific Classification
  • How things Work
  • How Real are Statistics? Four Possible Attitudes
  • EXTRACTS FROM The Growth of Cluster Analysis: Tryon, Ward, and Johnson
  • The Continuing Search for Order
  • Phenetic Taxonomy: Theory and Methods
  • Principles of Clustering
  • A Quantitative Approach to a Problem in Classification
  • Representation of Similarity Matrices by Trees
  • Data Clustering: A Review

VOLUME TWO: (USEFUL) KEY TEXTS

  • Introduction
  • Cluster Analysis in Perspective
  • The Practice of Cluster Analysis
  • A Review of Classification
  • Sociological Classification and Cluster Analysis
  • Cluster Analysis
  • Literature on Cluster-Analysis
  • Distance as a Measure of Taxonomic Similarity
  • Efficiency in Taxonomy
  • Numerical Taxonomy: Points of View
  • Hierarchical Grouping to Optimize an Objective Function
  • An Examination of Procedures for Determining the Number of Clusters in a Data Set
  • A Comparison of Some Methods of Cluster Analysis
  • A Nearest Centroid Technique for Evaluating the Minimum-variance Clustering Procedure
  • Measurement Problems in Cluster Analysis
  • Unresolved Problems in Cluster Analysis

VOLUME THREE: CLUSTER ANALYSIS IN PRACTICE

  • Introduction
  • The Use and Reporting of Cluster Analysis in Health Psychology: A Review
  • Cluster Analysis in Illness Perception Research: A Monte Carlo Study to Identify the Most Appropriate Method
  • The Psychiatric and Criminal Careers of Mentally Disordered Offenders Referred to a Custody Diversion Team in the United Kingdom
  • Fuzzy Cluster Analysis of Molecular Dynamics Trajectories
  • Mosaic: From an Area Classification System to Individual Classification
  • Creating the UK National Statistics 2001 Output Area Classification
  • Spatial Analysis Using Clustering Methods: Evaluating Central Point and Median Approaches
  • Use of Multiple Correspondence Analysis and Cluster Analysis to Study Dietary Behaviour: Food Consumption Questionnaire in the Su.Vi.Max. Cohort
  • Shopping-related Attitudes: a Factor and Cluster Analysis of Northern California Shoppers
  • Combining Cluster and Discriminant Analysis to Develop a Social Bond Topology of Runaway Youth
  • Heirarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method
  • Fuzzy Classification in Dynamic Environments
  • A Multistep Unsupervised Fuzzy Clustering Analysis of fMRI Time Series
  • A Note on K-modes Clustering
  • Using Self-Similarity to Cluster Large Data Sets
  • A Taxonomy of Similarity Mechanisms for Case-Based Reasoning
  • Using Case-based Approaches to Analyse Large Datasets: A Comparison of Ragin's fsQCA and Fuzzy Cluster Analysis
  • A Comparison of Cluster Analysis Techniques within a Sequential Validation Framework

VOLUME FOUR: DATA MINING WITH CLASSIFICATION

  • Introduction
  • Data Mining for Fun and Profit
  • Cluster Analysis using Data Mining Approach to Develop CRM Methodology to Assess the Customer Loyalty
  • Techniques of Cluster Algorithms in Data Mining
  • Data-Mining Discovery of Pattern and Process in Ecological Systems
  • Data Mining in Soft Computing Framework: A Survey
  • Data Mining and Internet Profiling: Emerging Regulatory and Technological Approaches
  • Statistical Classification Methods in Consumer Credit Scoring: A Review
  • Data Mining: An Overview from a Database Perspective
  • 50 Years of Data Mining and OR: Upcoming trends and Challenges
  • A General Framework for Mining Massive Data Streams
  • Confidence in Classification: A Bayesian Approach
  • Visualization Techniques for Mining Large Databases: A Comparison
  • Visualization of Fuzzy Clusters by Fuzzy Sammon Mapping Projection: Application to the Analysis of Phase Space Trajectories
  • Spatial-Temporal Data Mining Procedure: LASR
  • Turning Datamining into a Management Science Tool: New Algorithms and Empirical Results
  • Data Mining of Massive Datasets in Healthcare
  • Conclusion
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Cluster Analysis

David Byrne , Emma Uprichard
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September 2013 | 1584 pages | Sage UK

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Cluster analysis is a family of techniques that sorts - or more accurately, classifies - cases into groups of similar cases. 'Data mining' encompasses a whole host of methodological procedures that are used for cluster analysis while 'classification' that is the analytical catalyst to the methodological approach. Thinking about issues of 'classification', 'cluster analysis' and 'data mining' together in this four-volume collection is appropriate, therefore, specifically with regards to developing a case based 'attitude' to quantitative analysis.

This collection does not simply focus on a set of methods, but in presenting a range of existing work together, the logic of what is arguably a methodological phase-shift in quantitative research is exposed. In effect, this four-volume collection sets forth an analytical strategy which is increasingly, both implicitly and explicitly, acknowledged across the disciplines as being rooted in the exploratory and descriptive investigation of cases.

Bringing work on classification, cluster analysis and data mining together in a way that is both accessible and timely with respect to the level of 'activity' going on in each of these related areas is important to signal a step-change in the kind of data analysis that is currently taking place, nationally and internationally, and to facilitate further research by demarcating the methodological research where the cutting edge approaches to data analysis lie.

Volume One: The Classics

Volume Two: (Useful) Key Texts

Volume Three: Cluster Analysis in Practice

Volume Four: Data Mining with Classification


Table Of Contents:

  • VOLUME ONE: THE CLASSICS
  • Introduction
  • The Distinctiveness of Case-Oriented Research
  • The Causal Devolution
  • A Tradition of Natural Kinds
  • How "Natural" are "Kinds" of Sexual Orientation?'
  • The Logic of Classification
  • On the Logic of Classification
  • Scientific Classification
  • How things Work
  • How Real are Statistics? Four Possible Attitudes
  • EXTRACTS FROM The Growth of Cluster Analysis: Tryon, Ward, and Johnson
  • The Continuing Search for Order
  • Phenetic Taxonomy: Theory and Methods
  • Principles of Clustering
  • A Quantitative Approach to a Problem in Classification
  • Representation of Similarity Matrices by Trees
  • Data Clustering: A Review
  • VOLUME TWO: (USEFUL) KEY TEXTS
  • Introduction
  • Cluster Analysis in Perspective
  • The Practice of Cluster Analysis
  • A Review of Classification
  • Sociological Classification and Cluster Analysis
  • Cluster Analysis
  • Literature on Cluster-Analysis
  • Distance as a Measure of Taxonomic Similarity
  • Efficiency in Taxonomy
  • Numerical Taxonomy: Points of View
  • Hierarchical Grouping to Optimize an Objective Function
  • An Examination of Procedures for Determining the Number of Clusters in a Data Set
  • A Comparison of Some Methods of Cluster Analysis
  • A Nearest Centroid Technique for Evaluating the Minimum-variance Clustering Procedure
  • Measurement Problems in Cluster Analysis
  • Unresolved Problems in Cluster Analysis
  • VOLUME THREE: CLUSTER ANALYSIS IN PRACTICE
  • Introduction
  • The Use and Reporting of Cluster Analysis in Health Psychology: A Review
  • Cluster Analysis in Illness Perception Research: A Monte Carlo Study to Identify the Most Appropriate Method
  • The Psychiatric and Criminal Careers of Mentally Disordered Offenders Referred to a Custody Diversion Team in the United Kingdom
  • Fuzzy Cluster Analysis of Molecular Dynamics Trajectories
  • Mosaic: From an Area Classification System to Individual Classification
  • Creating the UK National Statistics 2001 Output Area Classification
  • Spatial Analysis Using Clustering Methods: Evaluating Central Point and Median Approaches
  • Use of Multiple Correspondence Analysis and Cluster Analysis to Study Dietary Behaviour: Food Consumption Questionnaire in the Su.Vi.Max. Cohort
  • Shopping-related Attitudes: a Factor and Cluster Analysis of Northern California Shoppers
  • Combining Cluster and Discriminant Analysis to Develop a Social Bond Topology of Runaway Youth
  • Heirarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method
  • Fuzzy Classification in Dynamic Environments
  • A Multistep Unsupervised Fuzzy Clustering Analysis of fMRI Time Series
  • A Note on K-modes Clustering
  • Using Self-Similarity to Cluster Large Data Sets
  • A Taxonomy of Similarity Mechanisms for Case-Based Reasoning
  • Using Case-based Approaches to Analyse Large Datasets: A Comparison of Ragin's fsQCA and Fuzzy Cluster Analysis
  • A Comparison of Cluster Analysis Techniques within a Sequential Validation Framework
  • VOLUME FOUR: DATA MINING WITH CLASSIFICATION
  • Introduction
  • Data Mining for Fun and Profit
  • Cluster Analysis using Data Mining Approach to Develop CRM Methodology to Assess the Customer Loyalty
  • Techniques of Cluster Algorithms in Data Mining
  • Data-Mining Discovery of Pattern and Process in Ecological Systems
  • Data Mining in Soft Computing Framework: A Survey
  • Data Mining and Internet Profiling: Emerging Regulatory and Technological Approaches
  • Statistical Classification Methods in Consumer Credit Scoring: A Review
  • Data Mining: An Overview from a Database Perspective
  • 50 Years of Data Mining and OR: Upcoming trends and Challenges
  • A General Framework for Mining Massive Data Streams
  • Confidence in Classification: A Bayesian Approach
  • Visualization Techniques for Mining Large Databases: A Comparison
  • Visualization of Fuzzy Clusters by Fuzzy Sammon Mapping Projection: Application to the Analysis of Phase Space Trajectories
  • Spatial-Temporal Data Mining Procedure: LASR
  • Turning Datamining into a Management Science Tool: New Algorithms and Empirical Results
  • Data Mining of Massive Datasets in Healthcare
  • Conclusion

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