Symbolic Data Analysis: Conceptual Statistics and Data Mining
This text presents a unified account of symbolic data, how they arise, and how they are structured. The reader is introduced to symbolic analytic methods described in the consistent statistical framework required to carry out such a summary and subsequent analysis.
- Presents a detailed overview of the methods and applications of symbolic data analysis.
- Includes numerous real examples, taken from a variety of application areas, ranging from health and social sciences, to economics and computing.
- Features exercises at the end of each chapter, enabling the reader to develop their understanding of the theory.
- Provides a supplementary website featuring links to download the SODAS software developed exclusively for symbolic data analysis, data sets, and further material.
Primarily aimed at statisticians and data analysts, Symbolic Data Analysis is also ideal for scientists working on problems involving large volumes of data from a range of disciplines, including computer science, health and the social sciences. There is also much of use to graduate students of statistical data analysis courses.
2. Symbolic Data.
2.1 Symbolic and Classical Data.
2.2 Categories, Concepts and Symbolic Objects.
2.3 Comparison of Symbolic and Classical Analysis.
3. Basic Descriptive Statistics: One Variate.
3.1 Some Preliminaries.
3.2 Multi-valued Variables.
3.3 Interval-valued Variables.
3.4 Multi-valued Modal variables.
3.5 Interval-valued Modal Variables.
4. Descriptive Statistics: Two or More Variates.
4.1 Multi-valued Variables.
4.2 Interval-valued Variables.
4.3 Modal Multi-valued Variables.
4.4 Modal Interval-valued Variables.
4.5 Baseball Interval-valued Dataset.
4.6 Measures of Dependence.
5. Principal Component Analysis.
5.1 Vertices Method.
5.2 Centers Method.
5.3 Comparison of the Methods.
6. Regression Analysis.
6.1 Classical Multiple Regression Model.
6.2 Multi-valued Variables.
6.3 Interval-valued Variables.
6.4 Histogram-valued Variables.
6.5 Taxonomy Variables.
6.6 Hierarchical Variables.
7. Cluster Analysis.
7.1 Dissimilarity and Distance Measures.
7.2 Clustering Structures.
7.4 Hierarchy-Divisive Clustering.
7.5 Hierarchy-Pyramid Clusters.
Edwin Diday is a Professor in Computer Science and Mathematics, at the Université Paris Dauphine, France. He is the author or editor of 14 previous books. He is also the founder of the symbolic data analysis field, and has led numerous international research teams in the area.