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

  • Jia, Wei (School of Information Science and Technology, Northwest University) ;
  • Hua, Qingyi (School of Information Science and Technology, Northwest University) ;
  • Zhang, Minjun (School of Information Science and Technology, Northwest University) ;
  • Chen, Rui (School of Information Science and Technology, Northwest University) ;
  • Ji, Xiang (School of Information Science and Technology, Northwest University) ;
  • Wang, Bo (School of Information Science and Technology, Northwest University)
  • Received : 2018.01.11
  • Accepted : 2018.07.02
  • Published : 2019.08.31

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.

Keywords

Intuitionistic Fuzzy Entropy Measure;Mobile User Interface Pattern;Particle Swarm Optimization;Population Search Strategy;Semi-Supervised Kernel Fuzzy C-Means

Acknowledgement

Supported by : National Natural Science Foundation of China, Northwest University

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