Compatible Clustering Algorithm with Convex Space Partitioning

Article Preview

Abstract:

In this paper, we discuss the problem of compatible clustering, we propose a new compatible clustering algorithm based on pNCompClu[9]. The new algorithm adopts space partitioning technique to replace the point neighborhood mechanism of pNCompClu. Experiments show that the proposed algorithm can get some consistent clustering results, and theory analysis also demonstrates that the proposed algorithm has higher clustering precision than pNCompClu has.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 468-471)

Pages:

147-151

Citation:

Online since:

February 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] R.Wan, L. Wang, M. Wang, X. Su. CNclustering: Clustering with compatible nucleoids. The IEEE 2009 4th International Conference of Computer Science & Education (ICCSE 2009), 2009, pp.797-800.

DOI: 10.1109/iccse.2009.5228158

Google Scholar

[2] Ji. Liu, Q. Zhang, W. Wang, L. McMillan, J. Prins. Clustering pari-wise dissimilarity data into partially ordered sets. Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Ddata Mining, 2006, pp.637-642.

DOI: 10.1145/1150402.1150480

Google Scholar

[3] J. Liu, Q. Zhang, W. Wang, L. McMillan, J. Prins. PoClustering: lossless clustering of dissimilarity data. Proceedings of the Seventh SIAM International Conference on Data Mining, 2007.

DOI: 10.1137/1.9781611972771.61

Google Scholar

[4] E.A. Socolovsky. A dissimilarity measure for clustering high and infinite dimensional data that satisfies the triangle inequality. NASA LaRC Technical Library Digital Repository, 2002, pp.1-12.

Google Scholar

[5] M.K. Ng, M.J. Li, J.Z. Huang, and Z. He. On the impact of dissimilarity measure in k-Modes clustering algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007,Vol.29, No. 3, pp.503-507.

DOI: 10.1109/tpami.2007.53

Google Scholar

[6] D.B. Hitchcock, Z. Chen. Smoothing dissimilarities to cluster binary data. Computational Statistics and Data Analysis, 2008.

DOI: 10.1016/j.csda.2008.03.012

Google Scholar

[7] P. Valtchev and J. Euzenat. Dissimilarity measure for collections of objects and values. Advances in Intelligent Data Analysis Reasoning about Data, 2006, pp.259-272..

DOI: 10.1007/bfb0052846

Google Scholar

[8] R. Wan, L. Wang, Z. Liu, X. Su. Clustering on compatible relation. Application Research of Computers (chinese), 2009, Vol. 26, No.4, pp.1303-1305.

Google Scholar

[9] R. Wan, L. Wang, Z. Hao. Clustering compatible objects by point neighborhood. 2010 International Conference on Artificial Intelligence and Education (2010 ICAIE), 2010, pp.171-174.

DOI: 10.1109/icaie.2010.5641428

Google Scholar