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. |
Contents
Theoretical Foundations | 3 |
Algorithms 25 4222 | 25 |
Text Mining | 87 |
Copyright | |
3 other sections not shown
Other editions - View all
Concept Data Analysis: Theory and Applications Claudio Carpineto,Giovanni Romano Limited preview - 2004 |
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
Add edge applications association rules attribute values bottom element browsing c₁ Carpineto closure operator closure system clustering complete lattice concept data analysis concept lattice conceptual clustering consider constraints contains context G context in Table cover CREDO data mining database described df my pl distance from sun example extent formal concept analysis frequent concepts frequent itemsets functional dependencies Giovanni Romano graph Hasse diagram hierarchy index terms infimum information retrieval input instance intent interaction interface inters intersection join-irreducible lhsSet lower neighbours Machine Learning many-valued attributes many-valued context maximal element Neighbours algorithm nextLevel node number of attributes number of concepts number of objects order-embedding ordered set ordering relation planets context query concept relevant representation Romano Section semilattice set of concepts set of implications shown in Figure ss dn structure subset text mining theoretical thesaurus top element Update upper bound visualization Y₁