A Text Document Clustering Method Based on Topical Concept

Nowadays, document clustering technology has been extensively used in text mining, information retrieval systems and etc. The conventional document clustering methods rely on the classical vector-space model using the key words as the feature. However, these methods ignore the semantic relations among the keywords, do not really address the special problems of document clustering: high dimensionality of the data, and high computation complexity. To solve these problems, based on topic concept clustering, this paper proposes a method for Chinese document clustering. In this paper, we introduce a novel topical document clustering method called Document Features Indexing Clustering (DFIC), which can identify topics accurately and cluster documents according to these topics. In DFIC, "topic elements" are defined and extracted for indexing base clusters. Additionally, document features are investigated and exploited. Experimental results show that DFIC can gain a higher precision (92.76%) than some widely used traditional clustering methods.