Mobile User Interface Pattern Clustering Using Improved Semi-Supervised Kernel Fuzzy Clustering Method

Wei Jia, Qingyi Hua, Minjun Zhang, Rui Chen, Xiang Ji and Bo Wang
Volume: 15, No: 4, Page: 986 ~ 1016, Year: 2019
10.3745/JIPS.04.0131
Keywords: Intuitionistic Fuzzy Entropy Measure, Mobile User Interface Pattern, Particle Swarm Optimization, Population Search Strategy, Semi-Supervised Kernel Fuzzy C-Means
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Abstract
Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.

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Cite this article
IEEE Style
W. Jia, Q. Hua, M. Zhang, R. Chen, X. Ji and B. Wang, "Mobile User Interface Pattern Clustering Using Improved Semi-Supervised Kernel Fuzzy Clustering Method," Journal of Information Processing Systems, vol. 15, no. 4, pp. 986~1016, 2019. DOI: 10.3745/JIPS.04.0131.

ACM Style
Wei Jia, Qingyi Hua, Minjun Zhang, Rui Chen, Xiang Ji, and Bo Wang. 2019. Mobile User Interface Pattern Clustering Using Improved Semi-Supervised Kernel Fuzzy Clustering Method, Journal of Information Processing Systems, 15, 4, (2019), 986~1016. DOI: 10.3745/JIPS.04.0131.