Concept Data Analysis: Theory and Applications / Edition 1

by Claudio Carpineto, Giovanni Romano

Overview

With the advent of the Web along with the unprecedented amount of information available in electronic format, conceptual data analysis is more useful and practical than ever, because this technology addresses important limitations of the systems that currently support users in their quest for information. Concept Data Analysis: Theory & Applications is the first book that provides a comprehensive treatment of the full range of algorithms available for conceptual data analysis, spanning creation, maintenance, display and manipulation of concept lattices.  The accompanying website allows you to gain a greater understanding of the principles covered in the book through actively working on the topics discussed.

The three main areas explored are interactive mining of documents or collections of documents (including Web documents), automatic text ranking, and rule mining from structured data.  The potentials of conceptual data analysis in the application areas being considered are further illustrated by two detailed case studies.

Concept Data Analysis: Theory & Applications is essential for researchers active in information processing and management and industry practitioners who are interested in creating a commercial product for conceptual data analysis or developing content management applications. 

Product Details

ISBN-13:	9780470850558
Publisher:	Wiley
Publication date:	09/03/2004
Pages:	220
Product dimensions:	6.80(w) x 9.90(h) x 0.70(d)

About the Author

Claudio Carpineto is the head of the Information mining group at Fondazione Ugo Bordoni, in Rome. He has published numerous articles on artificial intelligence.

Giovanni Romano is the author of Concept Data Analysis: Theory and Applications, published by Wiley.

Table of Contents

Foreword.

Preface.

I: THEORY AND ALGORITHMS.

1. Theoretical Foundations.

1.1 Basic Notions of Orders and Lattices.

1.2 Context, Concept, and Concept Lattice.

1.3 Many-valued Contexts.

1.4 Bibliographic Notes.

2. Algorithms.

2.1 Constructing Concept Lattices.

2.2 Incremental Lattice Update.

2.3 Visualization.

2.4 Adding Knowledge to Concept Lattices.

2.5 Bibliographic Notes.

II: APPLICATIONS.

3. Information Retrieval.

3.1 Query Modification.

3.2 Document Ranking

4. Text Mining.

4.1 Mining the Content of the ACM Digital Library.

4.2 MiningWeb Retrieval Results with CREDO.

4.3 Bibliographic Notes.

5. Rule Mining.

5.1 Implications.

5.2 Functional Dependencies.

5.3 Association Rules.

5.4 Classification Rules.

5.5 Bibliographic Notes.

References.

Index.