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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 ≤λ≤ 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.
Fei Hao; Doo-Soon Park; Shuai Li; Hwa Min Lee. Mining λ-Maximal Cliques from a Fuzzy Graph. Sustainability 2016, 8, 553 .
AMA StyleFei Hao, Doo-Soon Park, Shuai Li, Hwa Min Lee. Mining λ-Maximal Cliques from a Fuzzy Graph. Sustainability. 2016; 8 (6):553.
Chicago/Turabian StyleFei Hao; Doo-Soon Park; Shuai Li; Hwa Min Lee. 2016. "Mining λ-Maximal Cliques from a Fuzzy Graph." Sustainability 8, no. 6: 553.
During last few years we have witnessed a steady increase in medicine use for healthcare. The medicine experiences rated by other patients have huge potential to empower people to make more informed decisions. While the majority of previous research focused on rating prediction and recommendations on E-Commerce field, the area of healthcare or medical treatments has been rarely handled. Moreover, the geographical and temporal factors were not considered in their recommendation mechanisms. The rapid development of mobile devices, wireless networks, smart phones and ubiquitous wireless connections enable people to build and maintain mobile social interactions and relationships. In this paper, we identify and formalize the significant problem that exploits the over-the-counter medicine rating prediction and recommendation in mobile social networks. Then we devise the recommendation model and develop corresponding prototype of iDrug, reflecting a solution scheme of medicine rating prediction and recommendation in mobile social networks to increase the information accessibility for people’s decision support.
Shuai Li; Fei Hao; Mei Li; Hee-Cheol Kim. Medicine Rating Prediction and Recommendation in Mobile Social Networks. Computer Vision 2013, 216 -223.
AMA StyleShuai Li, Fei Hao, Mei Li, Hee-Cheol Kim. Medicine Rating Prediction and Recommendation in Mobile Social Networks. Computer Vision. 2013; ():216-223.
Chicago/Turabian StyleShuai Li; Fei Hao; Mei Li; Hee-Cheol Kim. 2013. "Medicine Rating Prediction and Recommendation in Mobile Social Networks." Computer Vision , no. : 216-223.