Abstract: Text clustering analysis is one of the commonly used methods in network information collection. As a selforganization, parallel clustering algorithm, antcolony clustering algorithm is widely used in clustering analysis. Aiming at avoiding the shortcomings of traditional antcolony clustering algorithm, such as lack of purpose, randomness and so on, a new improvement scheme is proposed. The “intelligent information center” mechanism is established, which owns some characters of dynamic global control of antcolony clustering process, enhancing the purpose of ants action, reducing the randomness. As a result, the proposed method can be utilized to improve the efficiency of the algorithm. Further, the purpose of optimizing the clustering algorithm is achieved by optimizing the parameters of the algorithm.

Key words: ant colony cluster algorithm, text clustering, intelligent information center, parameter optimization, self organization

摘要： 文本聚类分析是网络信息采集过程中常用的方法之一.蚁群聚类算法作为一种自组织、并行的聚类算法，被广泛应用于聚类分析中.针对传统蚁群聚类算法缺乏目的性、随机性过强等不足之处，提出了一种新的改进方案.设立“智能信息中心”动态全局地调控蚁群的聚类过程，增强了蚂蚁行动的目的性，减小其随机性，从而提高算法效率.进一步对算法参数进行优化，达到了优化文本聚类蚁群算法的目的.

关键词: 蚁群算法, 文本聚类, 智能信息中心, 参数优化, 自组织