Advances in Data Science

Symbolic, Complex, and Network Data

Edwin Diday, Rong Guan, Gilbert Saporta, Huiwen Wang, Edwin Diday, Rong Guan, Gilbert Saporta, Huiwen Wang

• Publisher: Wiley-ISTE
• Year: 2020
• Print ISBN: 9781786305763
• eBook ISBN: 9781119694960
• Edition: 1
• Topic: Business
• Subtopic: Management
• Index: Business

Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field.

Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences.

Part 1
Symbolic Data

1
Explanatory Tools for Machine Learning in the Symbolic Data Analysis Framework

The aim of this chapter is mainly to give explanatory tools for the understanding of standard, complex and big data. First, we recall some basic notions in Data Science: what are complex data? What are classes and classes of complex data? Which kind of internal class variability can be considered? Then, we define “symbolic data” and “symbolic data tables”, which express the within variability of classes, and we give some advantages of such kind of class description. Often in practice the classes are given. When they are not given, clustering can be used to build them by the Dynamic Clustering method (DCM) from which DCM regression, DCM canonical analysis, DCM mixture decomposition, and the like can be obtained. The description of these class yields by aggregation to a symbolic data table. We say that the description of a class is much more explanatory when it is described by symbolic variables (closer from the natural language of the users), and then by its usual analytical multidimensional description. The explanatory and characteristic power of classes can then be measured by criteria based on the symbolic data description of these classes and induce a way for comparing clustering methods by their explanatory power. These criteria are defined in a Symbolic Data Analysis framework for categorical variables, based on three random variables defined on the ground population. Tools are then given for ranking individuals, classes and their symbolic descriptive variables from the more toward the less characteristic. These characteristics are not only explanatory but can also express the concordance or the discordance of a class with the other classes. We suggest several directions of research mainly on parametric aspects of these criteria and on improving the explanatory power of Machine Learning tools. We finally present the conclusion and the wide domain of potential applications in socio demography, medicine, web security and so on.

1.1. Introduction

A “Data Scientist” is someone who is able to extract new knowledge from Standard, Big and Complex Data. Here we consider complex data as data that cannot be expressed in terms of a standard data table, where units are described by quantitative and qualitative variables. Complex data happen in case of unstructured data, unpaired samples, and multisource data (as mixture of numerical, textual, image and social networks data). The aggregation, fusion, and summarization of such data can be done into classes of row units that are considered as new units. Classes can be obtained by unsupervised learning, giving a concise and structured view on the data. In supervised learning, classes are used in order to provide efficient rules for the allocation of new units to a class. A third way is to consider classes as new units described by “symbolic” variables whose values are “symbols” as: intervals, probability distributions, weighted sequences of numbers or categories, functions, and the like, in order to express their within-class variability. For example, “Regions” express the variability of their inhabitant, “Companies” express the variability of their web intrusion, and “Species” express the variability of their specimen. One of the advantages of this approach is that unstructured data and unpaired samples at the level of row units become structured and paired at...

Table of contents

• Cover
• Table of Contents
• Preface
• Part 1: Symbolic Data
• Part 2: Complex Data
• Part 3: Network Data
• Part 4: Clustering
• List of Authors
• Index
• End User License Agreement