融合SOM和改进PSO的Web文档集成聚类算法

发布时间：2018-02-13 20:49

本文关键词： Web文档聚类自组织特征映射粗聚类改进PSO算法细聚类集成聚类算法　出处：《计算机工程与应用》2010年34期 　论文类型：期刊论文

【摘要】：随着信息的爆炸式增长,现有的搜索引擎在很多方面不能满足人们的需要。Web文档聚类可以减小搜索空间,加快检索速度,提高查询精度。提出了一种融合SOM(Self-Organizing Maps)粗聚类和改进PSO(Particle Swarm Optimization)细聚类的Web文档集成聚类算法。首先根据向量空间模型表示法,用特征词条及其权值表示Web文档信息,其次用SOM算法对文档特征集进行粗聚类,得到一组输出权值,然后用这组权值初始化改进的PSO算法,用改进PSO算法对此聚类结果进行细化,最终实现Web文档聚类。仿真结果表明,该算法能有效提高文档查询的查准率和查全率,具有一定的实用价值。
[Abstract]:With the explosive growth of information, the existing search engines can not meet the needs of people in many ways. Web document clustering can reduce the search space and speed up the search. In order to improve the query precision, this paper presents an algorithm for integrating SOM(Self-Organizing maps rough clustering and improving PSO(Particle Swarm optimization clustering. Firstly, according to vector space model representation, Web document information is represented by feature terms and their weights. Secondly, a set of output weights is obtained by coarse clustering of document feature sets using SOM algorithm, and then the improved PSO algorithm is initialized with this set of weights. The improved PSO algorithm is used to refine the clustering results. Finally, the Web document clustering is realized. The simulation results show that the algorithm can effectively improve the precision and recall of document query, and has certain practical value.
【作者单位】：湖南科技职业学院电子信息系;中南大学信息科学与工程学院;
【分类号】：TP391.1

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