Clustering Methodology for Symbolic Data Hardback

by Lynne (University of Georgia, Athens) Billard, Edwin (Universite de Paris IX - Dauphine, France) Diday

Part of the Wiley Series in Computational Statistics series

Hardback

Available to Order - This title is available to order, with delivery expected within 2 weeks

Description

Covers everything readers need to know about clustering methodology for symbolic data-including new methods and headings-while providing a focus on multi-valued list data, interval data and histogram data This book presents all of the latest developments in the field of clustering methodology for symbolic data-paying special attention to the classification methodology for multi-valued list, interval-valued and histogram-valued data methodology, along with numerous worked examples.

The book also offers an expansive discussion of data management techniques showing how to manage the large complex dataset into more manageable datasets ready for analyses. Filled with examples, tables, figures, and case studies, Clustering Methodology for Symbolic Data begins by offering chapters on data management, distance measures, general clustering techniques, partitioning, divisive clustering, and agglomerative and pyramid clustering. Provides new classification methodologies for histogram valued data reaching across many fields in data scienceDemonstrates how to manage a large complex dataset into manageable datasets ready for analysisFeatures very large contemporary datasets such as multi-valued list data, interval-valued data, and histogram-valued dataConsiders classification models by dynamical clusteringFeatures a supporting website hosting relevant data sets Clustering Methodology for Symbolic Data will appeal to practitioners of symbolic data analysis, such as statisticians and economists within the public sectors.

It will also be of interest to postgraduate students of, and researchers within, web mining, text mining and bioengineering.