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More About This Title Concept Data Analysis - Theory and Applications
English
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.
English
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.
English
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.
