Advances in data science: symbolic, complex, and network data

by Diday, E. Guan, Rong Saporta, G., (Gilbert) Wang, Huiwen

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Presenting a collection of up-to-date papers by eminent scholars following two international workshops held in Beijing and Paris, this book includes fundamental contributions, as well as applications to several domains, including business and the social sciences. --

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Title Statements

Full title :Advances in data science : symbolic, complex, and network data / edited by Edwin Diday, Rong Guan, Gilbert Saporta, Huiwen Wang.

Summary

Edition Statements

Publication Date:2020.

Publication Statements

Publisher(s):ISTE Ltd, Wiley
Place of Publication:London
Language:eng
Publication Date:2020.

The Physical Object

Description:1 online resource (253 pages)

Edition Identifiers

ISBN:1119694965, 9781119695110, 1119695112, 9781119694960, 9781119695103, 1119695104
DOI:10.1002/9781119695110
oclc:on1136969256
LC Classification:QA76.9.Q36
Dewey Decimal:001.4/2

Preface xi -- Part 1. Symbolic Data 1 -- Chapter 1. Explanatory Tools for Machine Learning in the Symbolic Data Analysis Framework 3 Edwin DIDAY -- 1.1. Introduction 4 -- 1.2. Introduction to Symbolic Data Analysis 6 -- 1.2.1. What are complex data? 6 -- 1.2.2. What are “classes” and “class of complex data”? 7 -- 1.2.3. Which kind of class variability? 7 -- 1.2.4. What are “symbolic variables” and “symbolic data tables”? 7 -- 1.2.5. Symbolic Data Analysis (SDA) 9 -- 1.3. Symbolic data tables from Dynamic Clustering Method and EM 10 -- 1.3.1. The “dynamical clustering method” (DCM) 10 -- 1.3.2. Examples of DCM applications 10 -- 1.3.3. Clustering methods by mixture decomposition 12 -- 1.3.4. Symbolic data tables from clustering 13 -- 1.3.5. A general way to compare results of clustering methods by the “explanatory power” of their associated symbolic data table 15 -- 1.3.6. Quality criteria of classes and variables based on the cells of the symbolic data table containing intervals or inferred distributions 15 -- 1.4. Criteria for ranking individuals, classes and their bar chart descriptive symbolic variables 16 -- 1.4.1. A theoretical framework for SDA 16 -- 1.4.2. Characterization …