A Novel Locality Sensitive K-Means Clustering Algorithm based on Core Clusters

Abstract:

The locality sensitive k-means clustering method has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some random samples are employed for the initial centers. In this paper, an initialization method based on the core clusters is used for the locality sensitive k-means clustering. The core clusters can be formed by constructing the σ-neighborhood graph and their centers are regarded as the initial centers of the locality sensitive k-means clustering. To investigate the effectiveness of our approach, several experiments are done on three datasets. Experimental results show that our proposed method can improve the clustering performance compared to the previous locality sensitive k-means clustering.

• Periodical: Applied Mechanics and Materials (Volumes 321-324)
• Pages: 1939-1942
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.321-324.1939
• Online since: June 2013
• Authors: Lei Gu*
• Keywords: Clustering Methods, Core Clusters, Initialization, Locality Sensitive K-Means

References

[1] M. Fillippone, F. Camastra, F. Masulli, S. Rovetta, A survey of kernel and spectral methods for clustering. Pattern Recognition, Vol.41, No.1 (2008), pp.176-190.

DOI: 10.1016/j.patcog.2007.05.018

[2] A.K. Jain, M.N. Murty, P.J. Flyn, Data clustering: a review. ACM Computing Surveys, Vol.31, No.3 (1999), pp.256-323.

[3] R. Xu, D. Wunsch, Survey of clustering algorithms. IEEE Transactions on Neural Net-works, Vol.16, No.3 (2005), pp.645-678.

[4] J.T. Tou, R.C. Gonzalez, Pattern recognition principles. Addison-Wesley, London (1974).

[5] J.C. Bezdek, Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981).

[6] D.W. Kim, K.Y. Lee, D. Lee, K.H. Lee, A kernel-based subtractive clustering method. Pattern Recognition Letters, Vol.26, No.7 (2005), pp.879-891.

DOI: 10.1016/j.patrec.2004.10.001

[7] T.M. Martinetz, S.G. Berkovich, K.J. Schulten, Neural-gas network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks, Vol.4, No.4, (1993), pp.558-569.

DOI: 10.1109/72.238311

[8] P.H. Huang, D.Q. Zhang, Locality sensitive c-means clustering algorithms. Neurocomputing, Vol.73, No.4, (2010), pp.2935-2943.

DOI: 10.1016/j.neucom.2010.07.015

[9] C. Omella, M. Anastasios, S. Sanhya, K. Don, L. Sijia, K.M. Philip, E. Radek, DifFUZZY: a fu- zzy clustering algorithm for complex datasets. International Journal of Computational Inte- lligence in Bioinformatics and Systems Biology, Vol.1, No.4, (2010), pp.402-417.

[10] J. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well sepa- rated clusters. Journal of Cybernet, No.3, (1974), pp.32-35.

DOI: 10.1080/01969727308546046

[11] UCI Machine Learning Repository: http://www.ics.uci.edu/~mlearn/MLSummary.html.