An Approach to Determine the Number of Clusters for Clustering Algorithms-Reference-Cited by-同舟云学术

An Approach to Determine the Number of Clusters for Clustering Algorithms

Published:2012 Issue: Volume: Page:485-494
ISSN:0302-9743
Container-title:Computational Collective Intelligence. Technologies and Applications
language:
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Author:

Nguyen Dinh Thuan,Doan Huan

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-34630-9_50.pdf

Reference6 articles.

1. Yan, M.S., Wu, K.L., Yu, J.: A novel fuzzy clustering algorithm. In: IEEE In’t Symp. on Computational Intelligence in Robotics and Automation, pp. 647–652 (2003)

2. Pham, D.T., Dimov, S.S., Nguyen, C.D.: Selection of K in K-means clustering. In: Proc. IMechE, Mechanical Engineering Science, vol. 219, pp. 103–119 (2005)

3. Yin, Z., Tang, Y., Sun, F., Sun, Z.: Fuzzy Clustering with Novel Separable Criterion Tsinghua Science and Technology, pp. 50–53 (2006) ISSN 1007-0214 09/2011

4. Haizhou, W., Mingzhou, S.: Optimal k-means Clustering in One Dimension by Dynamic Programming. The R Journal 3(2), 29–33 (2011) ISSN 2073-4859

5. Mohammad, F.E., Wesam, M.A.: Initializing K-Means Clustering Algorithm using Statistical Information. In’t Journal of Computer Applications, 51–55 (2011)

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