Computational Intelligence Methods for Clustering of SenseTagged Nepali Documents
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2015, Vol 17, Issue 1
Abstract
Abstract: This paper presents a method using hybridization of self organizing map (SOM ), particle swarmoptimization(PSO) and k-means clustering algorithm for document clustering. Document representation is animportant step for clustering purposes. The common way of represent a text is bag of words approach. Thisapproach is simple but has two drawbacks viz. synonymy and polysemy which arise because of the ambiguity ofthe words and the lack of information about the relations between the words. To avoid the drawbacks of bag ofwords approach words are tagged with senses in WordNet in this paper. Sense tagging of words provide exactsenses of words. Feature vectors are generated using sense tagged documents and clustering is carried outusing proposed hybrid SOM+PSO+K-means algorithm. In the proposed algorithm initially SOM is applied tothe feature vectors to produce the prototypes and then K-means clustering algorithm is applied to cluster theprototypes. 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.
Authors and Affiliations
Sunita Sarkar , Arindam Roy , Bipul Syam Purkayastha
Keywords
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- EP ID EP127150
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