A Particle Swarm Optimization based fuzzy c means approach for efficient web document clustering

There is a need to organize a large set of documents into categories through clustering so as to facilitate searching and finding the relevant information on the web with large number of documents becomes easier and quicker. Hence we need more efficient clustering algorithms for organizing documents. Clustering on large text dataset can be effectively done using partitional clustering algorithms. The Fuzzy C-means algorithm is the most suitable partitional clustering approach for handling large dataset with respect to execution time. This paper introduces a new Hybrid Particle Swarm Optimization method that combines the best features of PSO and fuzzy C-means algorithms for efficient web document clustering. We have tested this hybrid PSO algorithm on various text document collections. The document range varies from 512 to 1639 in the dataset and the terms ranges from 12367 to 19851. Based on the experimental results our proposed PSOFCM approach performs better clustering than other method. KeywordDocument clustering, PSO, Partitional clustering, Vector Space Model, Fuzzy C-means