Enhancing Document Clustering Using Condensing Cluster Terms and Fuzzy Association

Most document clustering methods are a challenging issue for improving clustering performance. Document clustering based on semantic features is highly efficient. However, the method sometimes did not successfully cluster some documents, such as highly articulated documents. In order to improve the clustering success of complex documents using semantic features, this paper proposes a document clustering method that uses terms of the condensing document clusters and fuzzy association to efficiently cluster specific documents into meaningful topics based on the document set. The proposed method improves the quality of document clustering because it can extract documents from the perspective of the terms of the cluster topics using semantic features and synonyms, which can also better represent the inherent structure of the document in connection with the document cluster topics. The experimental results demonstrate that the proposed method can achieve better document clustering performance than other methods.