Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : K Brindha | M Leelavathiunnamed

Volume 03 Issue 11 2016

ISSN no:  2348-3121

Page no: 26-28

Abstract – We use an optimization technique for K means ++ in order to apply the weight for the features to the bag of words mechanism derived using text mining or k means algorithm which fasten the cluster formation deletes the redundant features. Paralleled in order to improve the retrieval performance, In this Particle optimization algorithm (PSO) to reduce the number of iteration in the k means ++ algorithm. The PSO algorithm eliminates the semantic gap between the features in order to reduce the query time and precision of retrieval results. The PSO is capable to retrieving the data from the class which are even not found the training phase as it is works with huge work space. The Experimental results proves that novel proposed mechanism outperforms the state of art approaches in terms of precision , recall and f measures values which indicates the accuracy also in execution time which indicates the efficiency.  

Keywords— Information Search and Retrieval, Dictionary Learning, K-Means++ Clustering, Entropy Optimization, PSO Algorithm


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