Clustering Methodology for Symbolic Data

 
 
Standards Information Network (Verlag)
  • 1. Auflage
  • |
  • erschienen am 12. August 2019
  • |
  • 352 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-119-01038-8 (ISBN)
 
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.
1. Auflage
  • Englisch
  • Somerset
  • |
  • Großbritannien
John Wiley & Sons Inc
  • Für Beruf und Forschung
  • 4,51 MB
978-1-119-01038-8 (9781119010388)
weitere Ausgaben werden ermittelt
LYNNE BILLARD, PHD, is University Professor in the Department of Statistics at the University of Georgia, USA. She has over two hundred and twenty-five publications mostly in leading journals, and co-edited six books. Professor Billard is a former president of ASA, IBS, and ENAR.

EDWIN DIDAY, PHD, is the Professor of Computer Science at Centre De Recherche en Mathematiques de la Decision, CEREMADE, Universite Paris-Dauphine, Universite PSL, Paris, France. He has published fifty-eight papers and authored or edited fourteen books. Professor Diday is also the founder of the Symbolic Data Analysis field.
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Chapter 1 Introduction
  • Chapter 2 Symbolic Data: Basics
  • 2.1 Individuals, Classes, Observations, and Descriptions
  • 2.2 Types of Symbolic Data
  • 2.2.1 Multi-valued or Lists of Categorical Data
  • 2.2.2 Modal Multi-valued Data
  • 2.2.3 Interval Data
  • 2.2.4 Histogram Data
  • 2.2.5 Other Types of Symbolic Data
  • 2.3 How do Symbolic Data Arise?
  • 2.4 Descriptive Statistics
  • 2.4.1 Sample Means
  • 2.4.2 Sample Variances
  • 2.4.3 Sample Covariance and Correlation
  • 2.4.4 Histograms
  • 2.5 Other Issues
  • Exercises
  • Appendix
  • Chapter 3 Dissimilarity, Similarity, and Distance Measures
  • 3.1 Some General Basic Definitions
  • 3.2 Distance Measures: List or Multi-valued Data
  • 3.2.1 Join and Meet Operators for Multi-valued List Data
  • 3.2.2 A Simple Multi-valued Distance
  • 3.2.3 Gowda-Diday Dissimilarity
  • 3.2.4 Ichino-Yaguchi Distance
  • 3.3 Distance Measures: Interval Data
  • 3.3.1 Join and Meet Operators for Interval Data
  • 3.3.2 Hausdorff Distance
  • 3.3.3 Gowda-Diday Dissimilarity
  • 3.3.4 Ichino-Yaguchi Distance
  • 3.3.5 de Carvalho Extensisons of Ichino-Yaguchi Distances
  • 3.4 Other Measures
  • Exercises
  • Appendix
  • Chapter 4 Dissimilarity, Similarity, and Distance Measures: Modal Data
  • 4.1 Dissimilarity/Distance Measures: Modal Multi-valued List Data
  • 4.1.1 Union and Intersection Operators for Modal Multi-valued List Data
  • 4.1.2 A Simple Modal Multi-valued List Distance
  • 4.1.3 Extended Multi-valued List Gowda-Diday Dissimilarity
  • 4.1.4 Extended Multi-valued List Ichino-Yaguchi Dissimilarity
  • 4.2 Dissimilarity/Distance Measures: Histogram Data
  • 4.2.1 Transformation of Histograms
  • 4.2.2 Union and Intersection Operators for Histograms
  • 4.2.3 Descriptive Statistics for Unions and Intersections
  • 4.2.4 Extended Gowda-Diday Dissimilarity
  • 4.2.5 Extended Ichino-Yaguchi Distance
  • 4.2.6 Extended de Carvalho Distances
  • 4.2.7 Cumulative Density Function Dissimilarities
  • 4.2.8 Mallows' Distance
  • Exercises
  • Chapter 5 General Clustering Techniques
  • 5.1 Brief Overview of Clustering
  • 5.2 Partitioning
  • 5.3 Hierarchies
  • 5.4 Illustration
  • 5.5 Other Issues
  • Chapter 6 Partitioning Techniques
  • 6.1 Basic Partitioning Concepts
  • 6.2 Multi-valued List Observations
  • 6.3 Interval-valued Data
  • 6.4 Histogram Observations
  • 6.5 Mixed-valued Observations
  • 6.6 Mixture Distribution Methods
  • 6.7 Cluster Representation
  • 6.8 Other Issues
  • Exercises
  • Appendix
  • Chapter 7 Divisive Hierarchical Clustering
  • 7.1 Some Basics
  • 7.1.1 Partitioning Criteria
  • 7.1.2 Association Measures
  • 7.2 Monothetic Methods
  • 7.2.1 Modal Multi-valued Observations
  • 7.2.2 Non-modal Multi-valued Observations
  • 7.2.3 Interval-valued Observations
  • 7.2.4 Histogram-valued Observations
  • 7.3 Polythethic Methods
  • 7.4 Stopping Rule R
  • 7.5 Other Issues
  • Exercises
  • Chapter 8 Agglomerative Hierarchical Clustering
  • 8.1 Agglomerative Hierarchical Clustering
  • 8.1.1 Some Basic Definitions
  • 8.1.2 Multi-valued List Observations
  • 8.1.3 Interval-valued Observations
  • 8.1.4 Histogram-valued Observations
  • 8.1.5 Mixed-valued Observations
  • 8.1.6 Interval Observations with Rules
  • 8.2 Pyramidal Clustering
  • 8.2.1 Generality Degree
  • 8.2.2 Pyramid Construction Based on Generality Degree
  • 8.2.3 Pyramids from Dissimilarity Matrix
  • 8.2.4 Other Issues
  • Exercises
  • Appendix
  • References
  • Index
  • EULA

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