Concept Data Analysis : Theory and Applications

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Concept Data Analysis : Theory and Applications

$2,520.00

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內容描述

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Description:

The advent of the Web, along with the
unprecedented amount of data available in electronic format, has dramatically
increased the need for tools that support the users in retrieving,
understanding and mining the information and knowledge contained in such data.

Concept data analysis differs from statistical
data analysis in that the emphasis is on recognising and generalising the
structure of symbolic data through a mathematical representation termed a
concept lattice. Thanks to its simplicity, elegance and versatility, concept
data analysis can effectively support various kinds of content management
tasks using different or heterogeneous types of data.

Provides a comprehensive treatment of the full
range of techniques developed for concept data analysis covering creation,
maintenance, display and manipulation of concept lattices
Presents application areas such as information
retrieval and mining from text and web data as well as rule mining from
structured data
Features two detailed case studies; exploring
the content of the ACM Digital Library using an interface that integrates
multiple search functionalities; and mining web retrieval results through
the system CREDO, a version of which is available on-line for testing

Concept Data Analysis: Theory &
Applications is essential for researchers active in information processing
and data mining as well as industry practitioners who are interested in
creating a commercial product for concept data analysis or developing content
management applications. Computer science students will also find it
invaluable.

 

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 modi.cation.
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.

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