Classification

ISBN-13: 9781584880134 / Angielski / Twarda / 1999 / 272 str.

A. D. Gordon

Opis

This volume incorporates many methodologies developed since the publication of the first edition. Like its predecessor, it describes both the clustering and graphical methods of representing data, and offers advice on how to decide which methods of analysis best apply to a particular data set. It goes even further, however, by providing critical overviews of developments not widely known, including efficient clustering algorithms, cluster validation, consensus classifications, and the classification of symbolic data.

Spis treści

Introduction Classification, Assignment, and Dissection Aims of Classification Stages in a Numerical Classification Data Sets Measures of Similarity and Dissimilarity Introduction Selected Measures of Similarity and Dissimilarity Some Difficulties Construction of Relevant Measures Partitions Partitioning Criteria Iterative Relocation Algorithms Mathematical Programming Other Partitioning Algorithms How Many Clusters? Links with Statistical Models Hierarchical Classifications Definitions and Representations Algorithms Choice of Clustering Strategy Consensus Trees More General Tree Models Other Clustering Procedures Fuzzy Clustering Constrained Classification Overlapping Classification Conceptual Clustering Classification of Symbolic Data Partitions of Partitions Graphical Representations Introduction Principal Coordinates Analysis Non-Metric Multidimensional Scaling Interactive Graphics and Self-Organizing Maps Biplots Cluster Validation and Description Introduction Cluster Validation Cluster Description References Author Index Subject Index

O autorze

Gordon, A.D.