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论文类型：会议论文

发表时间：2014-07-06

收录刊物：EI、CPCI-S、Scopus

页面范围：2855-2860

关键字：particle swarm optimization; hybrid evolutionary algorithm; high-dimensional subspace clustering

摘要：Considering Particle Swarm Optimization (PSO) could enhance solutions generated during the evolution process by exploiting their social knowledge and individual memory, we used PSO as a local search strategy in Genetic Algorithm (GA) framework for fine tuning the search space. GA is to make sure that every region of the search space is covered so that we have a reliable estimate of the global optimal solution and PSO is for further pruning the good solutions by searching around the neighborhood. In this paper, proposed approach is used for subspace clustering, which is an extension of traditional clustering that seeks to find clustering in different subspaces within a dataset. Subspace clustering is to find a subset of dimensions on which to improve cluster quality by removing irrelevant and redundant dimensions in high dimensions problems. The experimental results demonstrate the positive effects of PSO as a local optimizer.