Computer Science ›› 2018, Vol. 45 ›› Issue (4): 220-226.doi: 10.11896/j.issn.1002-137X.2018.04.037

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Mobile Interface Pattern Clustering Algorithm Based on Improved Particle Swarm Optimization

JIA Wei, HUA Qing-yi, ZHANG Min-jun, CHEN Rui, JI Xiang and WANG Bo   

  • Online:2018-04-15 Published:2018-05-11

Abstract: Clustering is a very efficient method for analyzing information.Focusing on the issue that clustering results of the existing fuzzy C-means clustering algorithms based on particle swarm optimization are not good,a fuzzy C-means clustering algorithm based on improved particle swarm optimization was proposed and applied in mobile interface pattern clustering.Firstly,reasonable intuitionistic fuzzy entropy is constructed by using the geometric interpretation and the constraints of intuitionistic fuzzy entropy.Secondly,in the improved particle swarm optimization,the intuitionistic fuzzy entropy is used to measure the state of particle swarm,and chaotic opposition-based learning is used to improve the global search ability.Finally,the proposed algorithm employs the Gauss kernel function for enhancing nonlinear processing capability,and then it is applied in mobile interface pattern clustering.Experimental results show that the proposed clustering algorithm has better performance in mobile interface pattern clustering than the exis-ting clustering algorithms.

Key words: Particle swarm optimization,Mobile interface pattern,Clustering,Intuitionistic fuzzy entropy,Chaotic opposition-based learning

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