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Symbolic Data Analysis: Conceptual Statistics and Data Mining / Edition 1

Symbolic Data Analysis: Conceptual Statistics and Data Mining / Edition 1

by Lynne Billard, Edwin Diday


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Product Details

ISBN-13: 9780470090169
Publisher: Wiley
Publication date: 02/02/2007
Series: Wiley Series in Computational Statistics Series , #636
Pages: 330
Product dimensions: 6.30(w) x 9.02(h) x 0.94(d)

About the Author

Lynne Billard is a multi award winning University Professor of Statistics at the University of Georgia, USA. Her areas of interest include epidemic theory, AIDS, time series, sequential analysis, and symbolic data. A former President of the American Statistical Association as well as the ENAR Regional President and International President of the International Biometric Society, Professor Billard has co-edited 6 books, published over150 papers and been actively involved in many statistical societies and national committees.

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.

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Table of Contents

1. Introduction.


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.

7.3 Partitions.

7.4 Hierarchy-Divisive Clustering.

7.5 Hierarchy-Pyramid Clusters.

Data Index.

Author Index.

Subject Index.

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