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Clustering Methodology for Symbolic Data by Lynne Billard, Edwin Diday is a nonfiction available to read on EtoBox.

What is Clustering Methodology for Symbolic Data about?

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 science Demonstrates how to manage a large complex dataset into manageable datasets ready for analysis Features very large contemporary datasets such as multi

Who reads Clustering Methodology for Symbolic Data?

Author

Lynne Billard, Edwin Diday

Publisher

Wiley & Sons, Incorporated, John

Published

2019

Language

EN

ISBN

9781119010395

Category

nonfiction

Subjects

Mathematics, Stem