基于LDA的改进K-means算法在文本聚类中的应用  

Improved K-means algorithm based on latent Dirichlet allocation for text clustering

作　　者：王春龙[1] 张敬旭[2] 

机构地区：[1]华北电力大学控制与计算机工程学院,北京102206 [2]甘肃省电力公司,兰州730030

出　　处：《计算机应用》2014年第1期249-254,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61001197;61372182);国家电网公司科技项目(522722130292)

摘　　要：针对传统K-means算法初始聚类中心选择的随机性可能导致迭代次数增加、陷入局部最优和聚类结果不稳定现象的缺陷,提出一种基于隐含狄利克雷分布(LDA)主题概率模型的初始聚类中心选择算法。该算法选择蕴含在文本集中影响程度最大的前m个主题,并在这m个主题所在的维度上对文本集进行初步聚类,从而找到聚类中心,然后以这些聚类中心为初始聚类中心对文本集进行所有维度上的聚类,理论上保证了选择的初始聚类中心是基于概率可确定的。实验结果表明改进后算法聚类迭代次数明显减少,聚类结果更准确。The traditional K-means algorithm has an increasing number of iterations, and often falls into local optimal solution and unstable clustering since the initial cluster centers are randomly selected. To solve these problems, an initial clustering centers selection algorithm based on Latent Dirichlet Allocation （LDA） model for the K-means algorithm was proposed. In this improved algorithm, the top-m most important topics in text corpora were first selected. Then, the text corpora was preliminarily clustered based on the m dimensions of topics. As a result, the m cluster centers could be got in the algorithm, which were used to further make clustering on all the dimensions of the text corpora. Theoretically, the center for each cluster can be determined based on the probability without randomly selecting them. The experiment demonstrates that the clustering results of the improved algorithm are more accurate with smaller number of iterations.

关 键 词：主题模型 K-MEANS 聚类中心 文本聚类 隐含狄利克雷分布 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]