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Cluster Analysis (SAGE Benchmarks in Social Research Methods)

Editat de David Byrne, Emma Uprichard
Notă GoodReads:
en Limba Engleză Carte Hardback – 11 Jan 2012
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
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Specificații

ISBN-13: 9780857021281
ISBN-10: 0857021281
Pagini: 1584
Dimensiuni: 156 x 234 x 127 mm
Greutate: 0.00 kg
Ediția: Four-Volume Set
Editura: SAGE Publications
Colecția Sage Publications Ltd
Seria SAGE Benchmarks in Social Research Methods

Locul publicării: London, United Kingdom

Cuprins

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