Conceptual analysis of fuzzy data using FCA

Authors:
Kyoung-Mo Yang;Eung-Hee Kim;Suk-Hyung Hwang;Sung-Hee Choi
Affiliations:
Dept. of Computer Science & Engineering, SunMoon University, Asan-si, Chungnam, Korea;Biomedical Knowledge Engineering Laboratory, Seoul National University, Seoul, Korea;Dept. of Computer Science & Engineering, SunMoon University, Asan-si, Chungnam, Korea;Dept. of Computer Science & Engineering, SunMoon University, Asan-si, Chungnam, Korea
Venue:
ACS'08 Proceedings of the 8th conference on Applied computer scince
Year:
2008

Citing 4
Cited 0

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Abstract

In order to analyze vague data set of uncertainty information, Fuzzy Formal Concept Analysis(FFCA) incorporates fuzzy set theory into Formal Concept Analysis(FCA). It extracts useful information with a unit of fuzzy concept from given fuzzy formal context with a confidence threshold. Then it constructs fuzzy lattice by order relations between the fuzzy concepts. In this paper, we introduce basic notions of FCA and FFCA, and developed the Fuzzy Formal Concept Analysis Wizard(FFCA-Wizard), that supports FFCA's features. We demonstrate the process for discovering knowledge from uncertain data with our software, FFCA-Wizard. It can be applied some interesting areas such as traditional data mining, semantic web mining and so on.