Mining λ-Maximal Cliques from a Fuzzy Graph
Department of Computer Software Engineering, Soonchunhyang University, 22 Soonchunhyang-ro, Shinchang-myeon, Asan 31538, Korea
Department of Theoretical and Applied Sciences (DiSTA), University of Insubria, Via Mazzini 5, Varese 21100, Italy
Author to whom correspondence should be addressed.
Academic Editors: James Park and Han-Chieh Chao
Received: 30 April 2016 / Revised: 3 June 2016 / Accepted: 3 June 2016 / Published: 14 June 2016
The depletion of natural resources in the last century now threatens our planet and the life of future generations. For the sake of sustainable development, this paper pioneers an interesting and practical problem of dense substructure (i.e.
, maximal cliques) mining in a fuzzy graph where the edges are weighted by the degree of membership. For parameter 0
1 (also called fuzzy cut in fuzzy logic), a newly defined concept λ
-maximal clique is introduced in a fuzzy graph. In order to detect the λ
-maximal cliques from a fuzzy graph, an efficient mining algorithm based on Fuzzy Formal Concept Analysis (FFCA) is proposed. Extensive experimental evaluations are conducted for demonstrating the feasibility of the algorithm. In addition, a novel recommendation service based on an λ
-maximal clique is provided for illustrating the sustainable usability of the problem addressed.
This is an open access article distributed under the Creative Commons Attribution License
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MDPI and ACS Style
Hao, F.; Park, D.-S.; Li, S.; Lee, H.M. Mining λ-Maximal Cliques from a Fuzzy Graph. Sustainability 2016, 8, 553.
Hao F, Park D-S, Li S, Lee HM. Mining λ-Maximal Cliques from a Fuzzy Graph. Sustainability. 2016; 8(6):553.
Hao, Fei; Park, Doo-Soon; Li, Shuai; Lee, Hwa M. 2016. "Mining λ-Maximal Cliques from a Fuzzy Graph." Sustainability 8, no. 6: 553.
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