Mining λ-Maximal Cliques from a Fuzzy Graph

Title : Mining λ-Maximal Cliques from a Fuzzy Graph
Journal : Sustainability
Authors : Fei Hao, Doo-Soon Park, Shuai Li, Hwa Min Lee
Corresponding author : Doo-Soon Park

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 research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H8601-16-1009) supervised by the IITP (Institute for Information & communications Technology Promotion), and was partly supported by the Shanxi Scholarship Council of China (No. 2015-068) and National Nature Science Foundation of China (Grant No. 61372187).