Efficient Keyword Based Document Clustering Using Fuzzy C – Means Algorithm

Abstract

Clustering is an useful technique in the field of textual data mining. Cluster analysis divides objects into meaningful groups based on similarity between objects. The existing clustering approaches face the issues like practical applicability, very less accuracy, more classification time etc. In recent times, inclusion of fuzzy logic in clustering results in better clustering results. In order to further improve the performance of clustering, the  Fuzzy C-Means (FCMA) Algorithm is used. The keywords are extracted from the documents using LSA based document extraction. The Fuzzy partition matrix is created for the clustering process and the performance of the document clustering is greater based on the keyword when compared to the Existing K-Means Clustering and EM Algorithm. The proposed technique will be highly useful in the text mining process to increase the accuracy and performance of the text extraction process.