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"Business, government, and industry all need efficient and accurate methods of summarizing and extracting information from the huge amounts of data collected and stored electronically. Thoroughly updated, the second edition of this popular text presents graphical and clustering methods.
List of contents
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
About the author
Gordon, A.D.
Summary
"Business, government, and industry all need efficient and accurate methods of summarizing and extracting information from the huge amounts of data collected and stored electronically. Thoroughly updated, the second edition of this popular text presents graphical and clustering methods.
