Clustering Methodology for Symbolic Data

✍ Scribed by Lynne Billard, Edwin Diday

• Publisher: Wiley
• Year: 2019
• Tongue: English
• Leaves: 343
• Category: Library

✦ Table of Contents

Contents......Page 3
Introduction......Page 6
Symbolic Data Basics......Page 12
Individuals, Classes, Observations & Descriptions......Page 13
Types of Symbolic Data......Page 14
How do Symbolic Data arise......Page 22
Descriptive Statistics......Page 29
Other Issues......Page 43
Exercises......Page 44
General Basic Definitions......Page 52
Distance Measures - List or Multi-valued Data......Page 60
Distance Measures - Interval Data......Page 67
Exercises......Page 84
Dissimilarity/Distance Measures - Modal Multi-valued List Data......Page 88
Dissimilarity/Distance Measures - HistogramData......Page 98
Exercises......Page 123
Brief Overview of Clustering......Page 124
Partitioning......Page 125
Hierarchies......Page 130
Illustration......Page 136
Other Issues......Page 151
Partitioning Techniques......Page 153
Basic Partitioning Concepts......Page 154
Multi-valued List Observations......Page 157
Interval-valued Data......Page 163
Histogram Observations......Page 173
Mixed-valued Observations......Page 181
Mixture Distribution Methods......Page 183
Cluster Representation......Page 190
Other Issues......Page 193
Exercises......Page 195
Some Basics......Page 201
Monothetic Methods......Page 207
Polythethic Methods......Page 240
Stopping Rule......Page 254
Other Issues......Page 261
Exercises......Page 262
Agglomerative Hierarchical Clustering......Page 265
Pyramidal Clustering......Page 293
Exercises......Page 317
Appendix......Page 319
Refs......Page 321
Index......Page 334