Publisher 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 ClassicsVolume Two: (Useful) Key TextsVolume Three: Cluster Analysis in PracticeVolume Four: Data Mining with Classification

Author Biography

David Byrne is Emeritus Professor of Sociology and Applied Social Sciences at the University of Durham. He has published widely on the methodology of social research, for example, in Interpreting Quantitative Data (2002) and with Charles Ragin edited The SAGE Handbook of Case Based Methods (2009). His major theoretical engagement is with the deployment of the complexity frame of reference across the social sciences—see Complexity Theory and the Social Sciences: The State of the Art (with Gillian Callaghan, 2011) with a particular focus on application to policy and practice. His current research focus is on the implications of the transition to the post-industrial in welfare capitalism—Paying for the Welfare State in the 21st Century (with Sally Ruane, 2011) and Class After Industry (2018).

Table of Contents

VOLUME ONE: THE CLASSICSIntroduction - David Byrne and Emma UprichardThe Distinctiveness of Case-Oriented Research - C. RaginThe Causal Devolution - A. AbbottA Tradition of Natural Kinds - I. HackingHow "Natural" are "Kinds" of Sexual Orientation?′ - I. HackingThe Logic of Classification - W. L. DavidsonOn the Logic of Classification - G. SandriScientific Classification - J. DupréHow things Work - G. BowkerHow Real are Statistics? Four Possible Attitudes - A. DesrosièresEXTRACTS FROM The Growth of Cluster Analysis: Tryon, Ward, and Johnson - R. BlashfieldThe Continuing Search for Order - R. SokalPhenetic Taxonomy: Theory and Methods - R. SokalPrinciples of Clustering - W. T. WilliamsA Quantitative Approach to a Problem in Classification - C. Michener and R. SokalRepresentation of Similarity Matrices by Trees - J. A. HartiganData Clustering: A Review - A. Jain, M. Murty and P. FlynnVOLUME TWO: (USEFUL) KEY TEXTSIntroduction - David Byrne and Emma UprichardCluster Analysis in Perspective - D. SpeeceThe Practice of Cluster Analysis - J. KetteringA Review of Classification - R. CormackSociological Classification and Cluster Analysis - K. BaileyCluster Analysis - K. BaileyLiterature on Cluster-Analysis - R. K. Blashfield and M. S. AldenderferDistance as a Measure of Taxonomic Similarity - R. SokalEfficiency in Taxonomy - R. Sokal and P. SneathNumerical Taxonomy: Points of View - R. Sokal et alHierarchical Grouping to Optimize an Objective Function - J. WardAn Examination of Procedures for Determining the Number of Clusters in a Data Set - G. MilliganA Comparison of Some Methods of Cluster Analysis - J. C. GowerA Nearest Centroid Technique for Evaluating the Minimum-variance Clustering Procedure - R. M. McIntyre and R. K. BlashfieldMeasurement Problems in Cluster Analysis - D. G. MorrisonUnresolved Problems in Cluster Analysis - B. EverittVOLUME THREE: CLUSTER ANALYSIS IN PRACTICEIntroduction - David Byrne and Emma UprichardThe Use and Reporting of Cluster Analysis in Health Psychology: A Review - J. Clatworthy et alCluster Analysis in Illness Perception Research: A Monte Carlo Study to Identify the Most Appropriate Method - J. Clatworthy et alThe Psychiatric and Criminal Careers of Mentally Disordered Offenders Referred to a Custody Diversion Team in the United Kingdom - W. DyerFuzzy Cluster Analysis of Molecular Dynamics Trajectories - H. Gordon and R. SomorjaiMosaic: From an Area Classification System to Individual Classification - R. Webber and FarrCreating the UK National Statistics 2001 Output Area Classification - D. Vickers and P. ReesSpatial Analysis Using Clustering Methods: Evaluating Central Point and Median Approaches - A. MurrayUse of Multiple Correspondence Analysis and Cluster Analysis to Study Dietary Behaviour: Food Consumption Questionnaire in the Su.Vi.Max. Cohort - C. Guinot et alShopping-related Attitudes: a Factor and Cluster Analysis of Northern California Shoppers - P. Mokhtarian, D. Ory and X. CaoCombining Cluster and Discriminant Analysis to Develop a Social Bond Topology of Runaway Youth - A. CherryHeirarchical Clustering via Joint Between-Within Distances: Extending Ward′s Minimum Variance Method - G. Szekely and M. RizzoFuzzy Classification in Dynamic Environments - A. BouchachiaA Multistep Unsupervised Fuzzy Clustering Analysis of fMRI Time Series - M. Fadili et alA Note on K-modes Clustering - Z. Huang and M. NgUsing Self-Similarity to Cluster Large Data Sets - D. Barbará and P. ChenA Taxonomy of Similarity Mechanisms for Case-Based Reasoning - P. CunninghamUsing Case-based Approaches to Analyse Large Datasets: A Comparison of Ragin′s fsQCA and Fuzzy Cluster Analysis - B. Cooper and J. GlaesserA Comparison of Cluster Analysis Techniques within a Sequential Validation Framework - L. Morey, R. Blashfield and H. SkinnerVOLUME FOUR: DATA MINING WITH CLASSIFICATIONIntroduction - David Byrne and Emma UprichardData Mining for Fun and Profit - D. Hand et alCluster Analysis using Data Mining Approach to Develop CRM Methodology to Assess the Customer Loyalty - S. HosseiniTechniques of Cluster Algorithms in Data Mining - J. Grabner and A. RudolphData-Mining Discovery of Pattern and Process in Ecological Systems - M. Wesley et alData Mining in Soft Computing Framework: A Survey - Sushmita Mitra, Sankar K. Pal and Pabitra MitraData Mining and Internet Profiling: Emerging Regulatory and Technological Approaches - Ira S. Rubinstein, Ronald D. Lee and P. SchwartzStatistical Classification Methods in Consumer Credit Scoring: A Review - D. Hand and W. HenleyData Mining: An Overview from a Database Perspective - Ming-Syan Chen, Jiawei Han and Philip S. Yu50 Years of Data Mining and OR: Upcoming trends and Challenges - B. Baesens et alA General Framework for Mining Massive Data Streams - P. Domingos and G. HultenConfidence in Classification: A Bayesian Approach - W. Krazanowski et alVisualization Techniques for Mining Large Databases: A Comparison - Daniel Keim and Kriegel Hans-PeterVisualization of Fuzzy Clusters by Fuzzy Sammon Mapping Projection: Application to the Analysis of Phase Space Trajectories - B. Feil, B. Balasko and J. AbonyiSpatial-Temporal Data Mining Procedure: LASR - Xiaofeng WangTurning Datamining into a Management Science Tool: New Algorithms and Empirical Results - Lee Cooper and Giovanni GiuffridaData Mining of Massive Datasets in Healthcare - C. GoodallConclusion - David Byrne and Emma Uprichard

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