中文摘要：

针对传统K—Means聚类算法对初始聚类中心的选择敏感，易陷入局部最优解的问题，提出一种基于混合并行遗传算法的文本聚类方法。该方法首先将文档集合表示成向量空间模型，并在文档向量中随机选择初始聚类中心形成染色体，然后结合K—Means算法的高效性和并行遗传算法的全局优化能力，通过种群内的遗传、变异和种群间的并行进化、联姻，有效地避免了局部最优解的出现。实验表明该算法相对于K—Means算法、简单遗传算法等文本聚类方法具有更高的精确度和全局寻优能力。

英文摘要：

K-Means Clustering Algorithm is sensilive to the choice of the initial cluster center, easy to fall into a local optimal solution. In order to avoid this kind of flaw, we proposed Hybrid Parallel Genetic Algorithm. In this method, we expressed the documents set into Vector Space Model and randomly chose initial clustering centre to form chromosome among document vectors, then combined the efficiency of K-means Algorithm and the global optimization ability of Parallel Genetic Algorithm. Through heredity, variation in the community, and parallel evolution, getting married between communities, we can provide a higher efficiency and precision for text clustering. Experiments indicate that Hybrid Parallel Genetic Algorithm has higher accuracy and global optimization ability relative to the others text clustering method for example K-Means Algorithm, Genetic Algorithm and so on.