Premium
Discovery of time‐varying relations using fuzzy formal concept analysis and associations
Author(s) -
Martin Trevor,
Shen Yun,
Majidian Andrei
Publication year - 2010
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20450
Subject(s) - computer science , usable , simple (philosophy) , metadata , association rule learning , fuzzy logic , data mining , formal concept analysis , information overload , information retrieval , data science , artificial intelligence , world wide web , algorithm , philosophy , epistemology
Digital obesity, or information overload, is a widely recognized yet largely unsolved problem. Lack of metadata—that is, a useful and usable description of what is represented by data—is one of the fundamental obstacles preventing the wider use of computational intelligence techniques in tackling the problem of digital obesity. In this paper, we propose the use of fuzzy formal concept analysis to create simple taxonomies, which can be used to structure data and a novel form of fuzzy association rule to extract simple knowledge from data organized hierarchically according to the discovered taxonomies. The association strength is monitored over time, as data sets are updated. Feasibility of the methods is shown by applying them to a large (tens of thousands of entries) database describing reported incidents of terrorism. © 2010 Wiley Periodicals, Inc.