Document clustering is mainly used in the field of information retrieval. It play vital role in text clustering and web mining. In earlier days generative model based algorithms were used for text clustering. The main objective of document clustering is to group data elements such that the intra – group similarities are high and inter-group similarities are low. In this work, we address a new hybrid algorithm called MFMLK-Means for clustering TMG format document data. This algorithm is an improved version of our previous algorithm MLK-means clustering algorithm. The results of the proposed algorithm were compared with e probabilistic von Mises-Fisher model-based clustering (vMF-based k-means) and the previous MLK-means clustering algorithm. The improvements in the proposed algorithm are more significant.

Login to Download PDF

Please login with your Paper ID and password to access the full PDF.

🔑 Login to Download