Carpineto / Romano Concept Data Analysis
1. Auflage 2004
ISBN: 978-0-470-01128-7
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
Theory and Applications
E-Book, Englisch, 220 Seiten, E-Book
ISBN: 978-0-470-01128-7
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
With the advent of the Web along with the unprecedented amount ofinformation available in electronic format, conceptual dataanalysis is more useful and practical than ever, because thistechnology addresses important limitations of the systems thatcurrently support users in their quest for information. ConceptData Analysis: Theory & Applications is the first book thatprovides a comprehensive treatment of the full range of algorithmsavailable for conceptual data analysis, spanning creation,maintenance, display and manipulation of concept lattices. The accompanying website allows you to gain a greaterunderstanding of the principles covered in the book throughactively working on the topics discussed.
The three main areas explored are interactive mining ofdocuments 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 areasbeing considered are further illustrated by two detailed casestudies.
Concept Data Analysis: Theory & Applications isessential for researchers active in information processing andmanagement and industry practitioners who are interested increating a commercial product for conceptual data analysis ordeveloping content management applications.
Autoren/Hrsg.
Weitere Infos & Material
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.
