Application of topic analysis in document clustering

In order to solve the problem that features with high frequency can not be used to obtain clustering results with high quality cause of their incomplete reflection of the document's topic, and to obtain the nice description of information described by each cluster, lexical chains were used to reflect the document's topic and similar documents were clustered according to the similarity between different lexical chains. Then the lexical chains describing the same topic information in the same cluster were combined. Via analyzing the distribution of each topic clew among different clusters, the keyword sets that can completely reflect the topic of each cluster were extracted. Experimental results demonstrate that clustering results obtained by this method outperform those obtained by using features with high frequency to cluster documents, and the extracted keyword sets can reflect the emphasis information of each cluster.