Cluster analysis

Author(s)

• Byrne, D. S. (David S.)
• Uprichard, Emma

Bibliographic Information

Cluster analysis

edited by David Byrne and Emma Uprichard

（Sage benchmarks in social research methods series）

SAGE, 2012

• : set
• v. 1
• v. 2
• v. 3
• v. 4

Note

v. 1: Logic and classics, v. 2: (Useful) key texts, v. 3: Cluster analysis in practice, v. 4: Data mining with classification

Includes bibliographical references

Description and Table of Contents

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

Table of Contents

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. Dupre How things Work - G. Bowker How Real are Statistics? Four Possible Attitudes - A. Desrosieres 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. Barbara 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

by "Nielsen BookData"

Details

• NCID: BB08378968
• ISBN:
  • 9780857021281
• LCCN: 2011927524
• Country Code: us
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Los Angeles
• Pages/Volumes: 4 v.
• Size: 24 cm
• Classification:
  • DC23 : 519.53
• Subject Headings:
  • LCSH : Social sciences -- Statistical methods
  • LCSH : Cluster analysis
• Parent Bibliography ID:
  • BA60309623