An Enhanced Document Clustering Approach using Optimization Algorithm
P. Perumal, Raju Nedunchezhian, C. Gomathi · 2013
Fast and high quality document clustering is a crucial task in organizing information, search engine results, enhancing web crawling, and information retrieval or filtering. Recent studies have shown that the most commonly used partition-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. In the existing system, WordNet enabled W-k means clustering algorithm significantly improves standard k-means generating useful and high quality cluster tags but not time efficiency. In this system, a novel document clustering algorithm based on the Harmony Search (HS) optimization method is proposed. By modelling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time. Then, harmony clustering is integrated with the K-means algorithm to achieve better clustering. Contrary to the localized searching property of K-means algorithm, the proposed algorithms perform a globalized search in the entire solution space. Additionally, the proposed system improves K-means by making it less dependent on the initial parameters such as randomly chosen initial cluster centres, therefore, making it more stable. Experimental results reveal that the HS integrated with K-means algorithm converges to the best known optimum faster than other methods and the quality of clusters are comparable.