Clustering methodology for symbolic data

Resource Information

The work Clustering methodology for symbolic data represents a distinct intellectual or artistic creation found in European University Institute Library. This resource is a combination of several types including: Work, Language Material, Books.

Label

Clustering methodology for symbolic data

Statement of responsibility

Lynne Billard, Edwin Diday

Creator

• Billard, L., (Lynne), 1943-

Contributor

• Diday, E.

Author

• Diday, E.

Subject

• Cluster analysis
• Multivariate analysis

Language

eng

Summary

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. 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.--

Member of

• Wiley series in computational statistics

Assigning source

Provided by publisher

Illustrations

illustrations

Index

index present

Literary form

non fiction

Nature of contents

bibliography

Series statement

Wiley series in computational statistics