K-Means Clustering For Segment Web Search Results

Clustering is the power full technique for segment relevant data into different levels. This study has proposed K-means clustering method for cluster web search results for search engines. For represent documents we used vector space model and use cosine similarity method for measure similarity between user query and the search results. As an improvement of K-means clustering we used distortion curve method for identify optimal initial number of clusters.

1School of Computer Science and Technology, Wuhan University of   Technology, Wuhan, China, hasitha87@gmail.com.

2School of Economics, Wuhan University of Technology, Wuhan, China kapilar@sab.ac.lk.

3School of Management, Wuhan University of Technology, Wuhan, China skillangarathne@gmail.com.

4Faculty of Applied Sciences, Sabaragamuwa University of Sri Lanka, Balangoda, Sri Lanka, kapilar@sab.ac.lk.

Hasitha Indika Arumawadu, R. M. Kapila Tharanga Rathnayaka, S. K. Illangarathne "K-Means Clustering For Segment Web Search Results" International Journal of Engineering Works, Vol. 2, Issue 8, PP. 79-83, August 2015.                                                                                   

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