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-valued list
data, interval-valued data, and histogram-valued data
- Considers classification models by dynamical clustering
- Features 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.
