An Initial Point Selection Algorithm for K-Means Clustering

Le Qiang Bai; Yan Yao  Zhou; Shi Hong  Zhang

doi:10.4028/www.scientific.net/AMR.791-793.1289

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 791-793An Initial Point Selection Algorithm for K-Means...

An Initial Point Selection Algorithm for K-Means Clustering

Abstract:

Aiming at the problem of K-Means algorithm which is sensitive to select initial clustering center, this paper proposes a kind of initial point of K-Means algorithm. The algorithm processes the properties of the data objects, which determines the density of data object by counting the number of similar data objects and selects the center of categories according to the density of data object. The cluster numbers given and the UCI standard sets of data and the random data sets used, the clustering results demonstrate that the proposed algorithm has good stability, accuracy.

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Periodical:

Advanced Materials Research (Volumes 791-793)

Pages:

1289-1292

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.791-793.1289

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Online since:

September 2013

Authors:

Le Qiang Bai, Yan Yao Zhou, Shi Hong Zhang

Keywords:

Clustering Center, Data Mining (DM), Density, K-Means Algorithm

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References

[1] J. MacQueen: Proc of the 5th Berkeley Symposium on Mathematical Statistics and Probability (California, Berkeley January 7 1966). Vol. 1, p.154.

Google Scholar

[2] G.J. Mao, L.J. Duan and S. Wang: Data mining theory and algorithm (Beijing Tsinghua university press, China 2005).

Google Scholar

[3] K. Niu, S.B. Zhang and J.L. Chen: Journal of Beijing University of Posts and Telecommunications, Vol. 30 (2007) No. 2, p.6.

Google Scholar

[4] M. Huang, Z.S. He and L. Xin: Computer Engineering and Application, Vol. 47 (2011) No. 35, p.132.

Google Scholar

[5] L. Zhang, Y. Chen and Y. Ji: Application Research of Computers, Vol. 28 (2011) No. 11, p.4071.

Google Scholar

[6] A. Likas, N. Vlassis, J. J. Verbeek: Pattern Recognition, Vol. 36 (2003) No. 2, p.451.

Google Scholar

[7] S.S. Khan, A. Ahmad: Pattern Recognition Letters, Vol. 25 (2004) No. 11, p.1293.

Google Scholar

[8] G.P. Chen, W.P. Wang and J. Huang: Journal of Chinese Computer Systems, Vol. 33 (2012) No. 6, p.1320.

Google Scholar

[9] D.Q. Li, J.Y. Shen and H.M. Chen: 2008 IEEE Aerospace Conference (Big Sky, MT, United states, March 1- 8, 2008). Vol. 5, p.1.

Google Scholar

[10] W. Zhang, C.H. Liu and F.Y. Li: Computer Engineering, Vol. 20 (2005) No. 31, p.10.

Google Scholar