Concept Data Analysis
Theory and Applications
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The recent advances in the acquisition, storage, and transmission of data in digital format, as witnessed by the advent of the World Wide Web, have dramatically increased the need for tools that effectively support users in retrieving, understanding, and mining the information and knowledge contained in those data. Conceptual Data Analysis fulfills an important role by helping fill this gap. Elegancent and versatile, this title differs from statistical data analysis in that the emphasis is on recognizing and generalizing structural similarities, such as set inclusion relation from the data description, and not on mathematical manipulations of probability distributions.
With the advent of the Web along with the unprecedented amount of information available in electronic format, conceptual data analysis is more useful and practical than ever, because this technology addresses important limitations of the systems that currently support users in their quest for information. Concept Data Analysis: Theory & Applications is the first book that provides a comprehensive treatment of the full range of algorithms available for conceptual data analysis, spanning creation, maintenance, display and manipulation of concept lattices. The accompanying website allows you to gain a greater understanding of the principles covered in the book through actively working on the topics discussed.
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.
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.