Performance Comparison of Hard and Soft Approaches for Document Clustering

There is a tremendous spread in the amount of information on the largest shared information source like search engine. Fast and standards quality document clustering algorithms play an important role in helping users effectively towards vertical search engine, World Wide Web, summarizing & organizing information. Recent surveys have shown that partitional clustering algorithms are more suitable for clustering large datasets like World Wide Web. However the K-means algorithm is the most commonly used in partitional clustering algorithm because it can easily be implemented and most efficient interms of execution in time. In this paper, the authors represent a short overview of method for soft approaches of an optimal fuzzy document clustering algorithm as compare to the hard approaches.

Provided by: International Journal of Computer Applications Topic: Big Data Date Added: Mar 2012 Format: PDF

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