Candidate Cluster Extraction for Hierarchical Document Clustering

Text Document are tremendously increasing in the internet, the hierarchical document clustering has proven to be useful in grouping similar document for large applications. Still most documents suffer from problems of high dimensionality, scalability, accuracy and meaningful cluster labels. In this paper an new approach fuzzy frequent itemsets based hierarchical clustering is proposed, in which fuzzy association rule mining algorithm is used to improve the clustering accuracy. In this approach firstly the key terms are extracted from the document set and each document is preprocessed into the document representation for the further mining process. Secondly, a fuzzy association rule mining algorithm for text is discover to find the sets, highly related fuzzy frequent itemsets, in which the key terms are regarded as a labels of the candidate clusters. Referring the candidate cluster it has been experimentally evaluated based on classic 30, Classic 4, and tr11 data sets for two methods FIHC and K-means in MATLAB 2009Rb. KeywordsIntroduction, document clustering approach, document preprocessing, candidate cluster extraction, Fmeasure evaluation.