计算机工程Issue(11)：172-177,6.DOI:10.3969/j.issn.1000-3428.2014.11.034
基于改进粒子群优化的文本聚类算法研究
Research on Text Clustering Algorithm Based on Improved Particle Swarm Optimization
摘要
Abstract
Clustering result of k-means clustering algorithm is highly dependent on the choice of the initial cluster center. With regards to this, a text clustering algorithm based on improved Particle Swarm Optimization ( PSO ) is presented. Features of particle swarm algorithm and k-means algorithm are analysed. Considering the disadvantages of PSO including low solving precisions, high possibilities of being trapped in local optimization and premature convergence,self-regulating mechanism of inertia weight and cloud mutation operator are designed to improve PSO. Self-regulating mechanism of inertia weight adjusts the inertia weight dynamically according to the degree of the population evolution. Cloud mutation operator is based on stable tendency and randomness property of cloud model. The global best individual is used to complete mutation on particles. Those two algorithms are combined by taking advantages of power global search ability of PSO and strong capacity of local search of k-means. A particle is a group of clustering centers,and a sum of scatter within class is fitness function. Experimental results show that this algorithm is an accurate,efficient and stable text clustering algorithm.关键词
粒子群优化/自调节惯性权重机制/进化程度/云变异算子/k-means算法/文本聚类Key words
Particle Swarm Optimization ( PSO )/self-regulating mechanism of inertia weight/degree of evolution/cloud mutation operator/k-means algorithm/text clustering分类
信息技术与安全科学引用本文复制引用
王永贵,林琳,刘宪国..基于改进粒子群优化的文本聚类算法研究[J].计算机工程,2014,(11):172-177,6.基金项目
国家自然科学基金资助项目(60903082) （60903082）
辽宁省教育厅基金资助项目(L2012113)。 （L2012113）
