关键词: 文本聚类, 高维度, 粒子群优化（PSO）, 差分进化（DE）, [K]-均值

Abstract:

In the process of text clustering with high dimension and sparse features, Particle Swarm Optimization（PSO） algorithm easily falls into the local optimization in the later stage with the increase of algorithm iterations. A Differential Evolution（DE） strategy with better diversity is added to update the population and try to find a better global optimal solution. Meanwhile, considering the influence of the randomness of the centroids order among individuals on learning and updating individuals, a method of the self-adaptive adjustment of the centroids order is proposed, by which the centroid with the maximum similarity between individuals will be listed in the same cluster index as much as possible. Finally, through the test on the text datasets, the advantages of the proposed clustering Index adaptive DEPSO（IDEPSO） algorithm are verified, compared with other existing algorithms in internal and external indicators, and the effectiveness and feasibility of the algorithm are proved.

Key words: text clustering, high dimension, Particle Swarm Optimization（PSO）, Differential Evolution（DE）, [K]-means