Concept Data Analysis: Theory and ApplicationsWith 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. |
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Concept Data Analysis: Theory and Applications Claudio Carpineto,Giovanni Romano Limited preview - 2004 |
Concept Data Analysis: Theory and Applications Claudio Carpineto,Giovanni Romano No preview available - 2004 |
Common terms and phrases
ACM Digital Library Add edge applications association rules attribute values bottom element browsing c₁ closure clustering complete lattice complexity concept data analysis concept lattice concept lattice-based consider constraints contained context G context in Table CREDO currentLevel data mining database described df my pl extent formal concept analysis frequent concepts frequent itemsets functional dependencies Giovanni Romano graph Hasse diagram hierarchy index terms information retrieval Information Systems input instance intent interaction interface inters intersection JavaScript lhsSet lowBoundary lower neighbours Machine Learning many-valued context maximal element mining Nearest Neighbours algorithm Neighbours algorithm nested line diagram nextLevel node number of attributes number of concepts number of documents number of objects ordered set query concept query refinement ranking relevant representation Romano Section semilattice set of concepts set of implications shown in Figure ss dn subset text mining theoretical thesaurus top element Update upper bound visualization Y₁
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