一种基于群体智能的Web文档聚类算法

将群体智能聚类模型运用于文档聚类，提出了一种基于群体智能的Web文档聚类算法，首先运用向量空间模型表示Web文档信息，采用常规方法如消除无用词和特征词条约简法则得到文本特征集，然后将文档的向量随机分布到一个平面上，运用基于群体智能的聚类方法进行文档聚类，最后从平面上采用递归算法收集聚类结果，为了改善算法的实用性，将原算法与k均值算法结合提出一种混合聚类算法，通过实验比较，结果表明基于群体智能的Web文档聚类算法具有较好的聚类特性，它能将与一个主题相关的Web文档较完全而准确地聚成一类。

A CLUSTERING ALGORITHM BASED ON SWARM INTELLIGENCE FOR WEB DOCUMENT

Swarm intelligence due to its flexibility, robustness and self-organization has been applied in a variety of areas. A clustering algorithm based on swarm intelligence (CSI) for web documents is proposed. Firstly, web documents, which are denoted by vector space model with reduced document feature set, are randomly projected on a plane. Then, clustering analysis is conducted by a clustering method derived from a basic model interpreting ant colony organization of cemeteries. The artificial ants perform random walks on the plane and pick up or drop projected data items with the probability which is converted from swarm similarity within a local region by probability conversion function. Clusters are visually formed on the plane by ant colony collective actions in the absence of central controls. Finally, the clustering results are collected from the plane by a recursive algorithm. Each clustering center is labeled by the most weighted feature. A hybrid clustering algorithm CSIM is also proposed by combining the CSI with the k -means algorithm. CSIM inherits the prominent properties of both swarm intelligence and k -means. It also offsets the weakness of those two techniques. The experiment results and the comparison with other document clustering methods show that this web document clustering algorithm based on swarm intelligence has good clustering performance. The web documents focusing on a subject are rather completely and exactly clustering together.