Computational Intelligence Methods for Clustering of Sense Tagged Nepali Documents

This paper presents a method using hybridization of self organizing map (SOM ), particle swarm optimization(PSO) and k-means clustering algorithm for document clustering. Document representation is an important step for clustering purposes. The common way of represent a text is bag of words approach. This approach is simple but has two drawbacks viz. synonymy and polysemy which arise because of the ambiguity of the words and the lack of information about the relations between the words. To avoid the drawbacks of bag of words approach words are tagged with senses in WordNet in this paper. Sense tagging of words provide exact senses of words. Feature vectors are generated using sense tagged documents and clustering is carried out using proposed hybrid SOM+PSO+K-means algorithm. In the proposed algorithm initially SOM is applied to the feature vectors to produce the prototypes and then K-means clustering algorithm is applied to cluster the prototypes. Particle Swarm Optimization algorithm is used to find the initial centroid for K-means algorithm. Text documents in Nepali language are used to test the hybrid SOM+PSO+K-means clustering algorithm.