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| k-Nearest-Neighbor Network Based Data Clustering Algorithm |
| JIN Di1,2,LIU Jie1,2,3,JIA Zheng-Xue4,LIU Da-You1,2 |
1.College of Computer Science and Technology,Jilin University,Changchun 130012
2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012
3.Shanghai Key Laboratory of Intelligent Information Processing,Fudan University,Shanghai 200433
4.FAW VW Automobile Co.,Ltd.,Changchun 130012 |
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Abstract Data clustering is a hotspot in data mining area. Though there have been lots of data clustering algorithms now, the clustering accuracy of them is far from perfect. A structural similarity based network clustering algorithm (SSNCA) is proposed in this paper, which attempt to further improve the data clustering accuracy from the view of network clustering. The concrete solution scheme is that vector dataset for clustering is converted to a k-Nearest-Neigborhood network and SSNCA is used to cluster this network. Comparing SSNCA with the algorithms of c-Means and affinity propagation (AP), experimental result shows that the fitness value got by the proposed algorithm is a little worse than AP, but its clustering accuracy is obviously better than that of the other two algorithms.
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Received: 27 April 2009
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2010, Vol. 23 
