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Symbolic Data Analysis: Conceptual Statistics and Data Mining

Publisher: 
John Wiley
Number of Pages: 
321
Price: 
100.00
ISBN: 
0470090162
Date Received: 
Friday, January 26, 2007
Reviewable: 
No
Include In BLL Rating: 
No
Reviewer Email Address: 
Lynne Billard and Edwin Diday
Series: 
Wiley Series in Computational Statistics
Publication Date: 
2007
Format: 
Hardcover
Category: 
Monograph

1. Introduction.

References.

2. Symbolic Data.

2.1 Symbolic and Classical Data.

2.1.1 Types of Data.

2.1.2 Dependencies in the Data.

2.2 Categories, Concepts and Symbolic Objects.

2.2.1 Preliminaries.

2.2.2 Descriptions, Assertions, Extents.

2.2.3 Concepts of Concepts.

2.2.4 Some Philosophical Aspects.

2.2.5 Fuzzy, Imprecise, and Conjunctive Data.

2.3 Comparison of Symbolic and Classical Analysis.

Exercises.

References.

Tables.

Figures.

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.

Exercises.

References.

Tables.

Figures.

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.

4.5.1 The Data: Actual and Virtual Datasets.

4.5.2 Joint Histograms.

4.5.3 Guiding Principles.

4.6 Measures of Dependence.

4.6.1 Moment Dependence.

4.6.2 Spearman’s rho and copulas.

Exercises.

References.

Tables.

Figures.

5. Principal Component Analysis.

5.1 Vertices Method.

5.2 Centers Method.

5.3 Comparison of the Methods.

Exercises.

References.

Tables.

Figures.

6. Regression Analysis.

6.1 Classical Multiple Regression Model.

6.2 Multi-valued Variables.

6.2.1 Single Dependent Variable.

6.2.2 Multi-valued Dependent Variable.

6.3 Interval-valued Variables.

6.4 Histogram-valued Variables.

6.5 Taxonomy Variables.

6.6 Hierarchical Variables.

Exercises.

References.

Tables.

Figures.

7. Cluster Analysis.

7.1 Dissimilarity and Distance Measures.

7.1.1 Basic Definitions.

7.1.2 Multi-valued Variables.

7.1.3 Interval-valued Variables.

7.1.4 Mixed-valued Variables.

7.2 Clustering Structures.

7.2.1 Types of Clusters: Definitions.

7.2.2 Construction of Clusters: Building Algorithms.

7.3 Partitions.

7.4 Hierarchy-Divisive Clustering.

7.4.1 Some Basics.

7.4.2 Multi-valued Variables.

7.4.3 Interval-valued Variables.

7.5 Hierarchy-Pyramid Clusters.

7.5.1 Some Basics.

7.5.2 Comparison of Hierarchy and Pyramid Structures.

7.5.3 Construction of Pyramids.

Exercises.

References.

Tables.

Figures.

Data Index.

Author Index.

Subject Index.

Publish Book: 
Modify Date: 
Saturday, September 1, 2007

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