Research Article
A Parameter Free Clustering Algorithm
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 164 - Issue 1 |
| Published: Apr 2017 |
| Authors: Omar Kettani, Faical Ramdani |
10.5120/ijca2017913574
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Omar Kettani, Faical Ramdani . A Parameter Free Clustering Algorithm. International Journal of Computer Applications. 164, 1 (Apr 2017), 34-39. DOI=10.5120/ijca2017913574
@article{ 10.5120/ijca2017913574,
author = { Omar Kettani,Faical Ramdani },
title = { A Parameter Free Clustering Algorithm },
journal = { International Journal of Computer Applications },
year = { 2017 },
volume = { 164 },
number = { 1 },
pages = { 34-39 },
doi = { 10.5120/ijca2017913574 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2017
%A Omar Kettani
%A Faical Ramdani
%T A Parameter Free Clustering Algorithm%T
%J International Journal of Computer Applications
%V 164
%N 1
%P 34-39
%R 10.5120/ijca2017913574
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In data mining, most of clustering algorithms either require that the user provides in advance the exact number of clusters, or to tune some input parameter, which is often a difficult task. The present paper intends to overcome this problem by proposing a parameter free algorithm for automatic clustering. We evaluated its performance by applying on several benchmark datasets. Experimental results demonstrated that the proposed approach is effective.
References
- Lloyd., S. P. (1982). "Least squares quantization in PCM". IEEE Transactions on Information Theory 28 (2): 129–137. doi:10.1109/TIT.1982.1056489.
- Kettani, O. ; Ramdani, F. & Tadili, B. An Agglomerative Clustering Method for Large Data Sets.International Journal of Computer Applications 92(14):1-7, April 2014. DOI:10.5120/16074-4952
- Asuncion, A. and Newman, D.J. (2007). UCI Machine LearningRepository [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, School of Information and Computer Science.
- Aloise, D.; Deshpande, A.; Hansen, P.; Popat, P. (2009). "NP-hardness of Euclidean sum-of-squares clustering". Machine Learning 75: 245–249. doi:10.1007/s10994-009-5103-0.
- Dan Pelleg and Andrew Moore. X-means: Extending k-means with efficient estimation of the number of clusters. In Proceedings of the 17th International Conf. on Machine Learning, pages 727–734. Morgan Kaufmann, 2000.
- Robert Tibshirani, Guenther Walther, and Trevor Hastie. Estimating the number of clusters in a dataset via the Gap statistic. Journal of the Royal Statistical Society B, 63:411–423, 2001.
- Greg Hamerly and Charles Elkan. Learning the k in k-means. In Proceedings of the seventeenth annual conference on neural information processing systems (NIPS), pages 281–288, 2003
- M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” in Proc. Second Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD), 1996
- Pal, N.R. and Bezdek, J.C. (1995) On Cluster Validity for the Fuzzy c-Means Model. IEEE Transactions on Fuzzy Systems, 3, 370-379. http://dx.doi.org/10.1109/91.413225
- T. Calinski and J. Harabasz. A dendrite method for cluster analysis. Communications in Statistics, 3:1–27, 1974.
- G. W. Milligan and M. C. Cooper. An examination of procedures for determining the number of clusters in a data set. Psychometrica, 50:159–179, 1985.
- L. Kaufman and P. J. Rousseeuw. Finding groups in Data: “an Introduction to Cluster Analysis”. Wiley, 1990.
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