| 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. Blashfield |
| Measurement Problems in Cluster Analysis | D. G. Morrison |
| Unresolved Problems in Cluster Analysis | B. Everitt |
| VOLUME THREE: CLUSTER ANALYSIS IN PRACTICE | |
| Introduction | David Byrne and Emma Uprichard |
| The Use and Reporting of Cluster Analysis in Health Psychology: A Review | J. Clatworthy et al |
| Cluster Analysis in Illness Perception Research: A Monte Carlo Study to Identify the Most Appropriate Method | J. Clatworthy et al |
| The Psychiatric and Criminal Careers of Mentally Disordered Offenders Referred to a Custody Diversion Team in the United Kingdom | W. Dyer |
| Fuzzy Cluster Analysis of Molecular Dynamics Trajectories | H. Gordon and R. Somorjai |
| Mosaic: From an Area Classification System to Individual Classification | R. Webber and Farr |
| Creating the UK National Statistics 2001 Output Area Classification | D. Vickers and P. Rees |
| Spatial Analysis Using Clustering Methods: Evaluating Central Point and Median Approaches | A. Murray |
| Use of Multiple Correspondence Analysis and Cluster Analysis to Study Dietary Behaviour: Food Consumption Questionnaire in the Su.Vi.Max. Cohort | C. Guinot et al |
| Shopping-related Attitudes: a Factor and Cluster Analysis of Northern California Shoppers | P. Mokhtarian, D. Ory and X. Cao |
| Combining Cluster and Discriminant Analysis to Develop a Social Bond Topology of Runaway Youth | A. Cherry |
| Heirarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method | G. Szekely and M. Rizzo |
| Fuzzy Classification in Dynamic Environments | A. Bouchachia |
| A Multistep Unsupervised Fuzzy Clustering Analysis of fMRI Time Series | M. Fadili et al |
| A Note on K-modes Clustering | Z. Huang and M. Ng |
| Using Self-Similarity to Cluster Large Data Sets | D. Barbará and P. Chen |
| A Taxonomy of Similarity Mechanisms for Case-Based Reasoning | P. Cunningham |
| Using Case-based Approaches to Analyse Large Datasets: A Comparison of Ragin's fsQCA and Fuzzy Cluster Analysis | B. Cooper and J. Glaesser |
| A Comparison of Cluster Analysis Techniques within a Sequential Validation Framework | L. Morey, R. Blashfield and H. Skinner |
| VOLUME FOUR: DATA MINING WITH CLASSIFICATION | |
| Introduction | David Byrne and Emma Uprichard |
| Data Mining for Fun and Profit | D. Hand et al |
| Cluster Analysis using Data Mining Approach to Develop CRM Methodology to Assess the Customer Loyalty | S. Hosseini |
| Techniques of Cluster Algorithms in Data Mining | J. Grabner and A. Rudolph |
| Data-Mining Discovery of Pattern and Process in Ecological Systems | M. Wesley et al |
| Data Mining in Soft Computing Framework: A Survey | Sushmita Mitra, Sankar K. Pal and Pabitra Mitra |
| Data Mining and Internet Profiling: Emerging Regulatory and Technological Approaches | Ira S. Rubinstein, Ronald D. Lee and P. Schwartz |
| Statistical Classification Methods in Consumer Credit Scoring: A Review | D. Hand and W. Henley |
| Data Mining: An Overview from a Database Perspective | Ming-Syan Chen, Jiawei Han and Philip S. Yu |
| 50 Years of Data Mining and OR: Upcoming trends and Challenges | B. Baesens et al |
| A General Framework for Mining Massive Data Streams | P. Domingos and G. Hulten |
| Confidence in Classification: A Bayesian Approach | W. Krazanowski et al |
| Visualization Techniques for Mining Large Databases: A Comparison | Daniel Keim and Kriegel Hans-Peter |
| Visualization of Fuzzy Clusters by Fuzzy Sammon Mapping Projection: Application to the Analysis of Phase Space Trajectories | B. Feil, B. Balasko and J. Abonyi |
| Spatial-Temporal Data Mining Procedure: LASR | Xiaofeng Wang |
| Turning Datamining into a Management Science Tool: New Algorithms and Empirical Results | Lee Cooper and Giovanni Giuffrida |
| Data Mining of Massive Datasets in Healthcare | C. Goodall |
| Conclusion | David Byrne and Emma Uprichard |
