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Klappentext 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 Zusammenfassung This collection considers issues of 'classification', 'cluster analysis' and 'data mining' together, presenting a range of existing work together in an accessible way, and demonstrating a methodological phase-shift in the kind of data analysis that is currently taking place, nationally and internationally. Inhaltsverzeichnis VOLUME ONE: THE CLASSICS Introduction - David Byrne and Emma Uprichard The Distinctiveness of Case-Oriented Research - C. Ragin The Causal Devolution - A. Abbott A Tradition of Natural Kinds - I. Hacking How "Natural" are "Kinds" of Sexual Orientation?¿ - I. Hacking The Logic of Classification - W. L. Davidson On the Logic of Classification - G. Sandri Scientific Classification - J. Dupré How things Work - G. Bowker How Real are Statistics? Four Possible Attitudes - A. Desrosières EXTRACTS FROM The Growth of Cluster Analysis: Tryon, Ward, and Johnson - R. Blashfield The Continuing Search for Order - R. Sokal Phenetic Taxonomy: Theory and Methods - R. Sokal Principles of Clustering - W. T. Williams A Quantitative Approach to a Problem in Classification - C. Michener and R. Sokal Representation of Similarity Matrices by Trees - J. A. Hartigan Data Clustering: A Review - A. Jain, M. Murty and P. Flynn VOLUME TWO: (USEFUL) KEY TEXTS Introduction - David Byrne and Emma Uprichard Cluster Analysis in Perspective - D. Speece The Practice of Cluster Analysis - J. Kettering A Review of Classification - R. Cormack Sociological Classification and Cluster Analysis - K. Bailey Cluster Analysis - K. Bailey Literature on Cluster-Analysis - R. K. Blashfield and M. S. Aldenderfer Distance as a Measure of Taxonomic Similarity - R. Sokal Efficiency in Taxonomy - R. Sokal and P. Sneath Numerical Taxonomy: Points of View - R. Sokal et al Hierarchical Grouping to Optimize an Objective Function - J. Ward An Examination of Procedures for Determining the Number of Clusters in a Data Set - G. Milligan A Comparison of Some Methods of Cluster Analysis - J. C. Gower A Nearest Centroid Technique for Evaluating the Minimum-variance Clustering Procedure - R. M. McIntyre and R. K. Blashfiel...
