Description

Book Synopsis
Offers a treatment of the full range of algorithms available for conceptual data analysis, spanning creation, maintenance, display and manipulation of concept lattices. The website accompanying this book allows you to gain a better understanding of the principles covered herein through working on the topics discussed.

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.

Concept Data Analysis

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A Hardback by Claudio Carpineto, Giovanni Romano

15 in stock


    View other formats and editions of Concept Data Analysis by Claudio Carpineto

    Publisher: John Wiley & Sons Inc
    Publication Date: 27/07/2004
    ISBN13: 9780470850558, 978-0470850558
    ISBN10: 0470850558

    Description

    Book Synopsis
    Offers a treatment of the full range of algorithms available for conceptual data analysis, spanning creation, maintenance, display and manipulation of concept lattices. The website accompanying this book allows you to gain a better understanding of the principles covered herein through working on the topics discussed.

    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.

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