Concept Data Analysis: Theory and Applications
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.
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.
2.1 Constructing Concept Lattices.
2.2 Incremental Lattice Update.
2.4 Adding Knowledge to Concept Lattices.
2.5 Bibliographic Notes.
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.
5.2 Functional Dependencies.
5.3 Association Rules.
5.4 Classification Rules.
5.5 Bibliographic Notes.
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