An Efficient Document Clustering by Optimization Technique for Cluster Optimality

clustering grows to be a very famous technique with the popularity of the web which also indicates that quick and best clustering technique acts as an important issue. Document clustering is about identifying semantically interconnected groups from formless collection of text documents. Feature Selection is significant for clustering process because number of the isolated or redundant feature should misguide the clustering results. Existing work presented improved Niching memetic algorithm and improved Genetic algorithm (GA) for feature selection. To attain more perfect document clustering, more instructive features including optimal conceptual weight are essential. In this paper, the proposed work presents the optimization technique to evaluate the cluster optimality for efficient document clustering based on the optimized conceptual feature words. The conceptual words (similarity words) are extracted from the featured words by using feature selection process. The important of cluster words are identified by the optimal conceptual word weight values. Experiments are carried out to evaluate the proposed optimization technique for efficient document clustering in terms of Conceptual word weight, Number of conceptual words and optimal conceptual word weight. Keywordsclustering, Conceptual words, cluster optimality