Open Access   Article

Determination of Optimal Number of Clusters in Cure Using Representative Points

Khumukcham Robindro1 , Bisheshwar Khumukcham2 , Ksh. Nilakanta Singh3

1 Department of Computer Science, Manipur University, Canchipur, Imphal, Manipur, India.
2 Department of Computer Science, Manipur University, Canchipur, Imphal, Manipur, India.
3 Department of Computer Science, Manipur University, Canchipur, Imphal, Manipur, India.

Correspondence should be addressed to: rbkh@manipuruniv.ac.in.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-2 , Page no. 313-320, Feb-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i2.313320

Online published on Feb 28, 2018

Copyright © Khumukcham Robindro, Bisheshwar Khumukcham, Ksh. Nilakanta Singh . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: Khumukcham Robindro, Bisheshwar Khumukcham, Ksh. Nilakanta Singh, “Determination of Optimal Number of Clusters in Cure Using Representative Points”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.313-320, 2018.

MLA Style Citation: Khumukcham Robindro, Bisheshwar Khumukcham, Ksh. Nilakanta Singh "Determination of Optimal Number of Clusters in Cure Using Representative Points." International Journal of Computer Sciences and Engineering 6.2 (2018): 313-320.

APA Style Citation: Khumukcham Robindro, Bisheshwar Khumukcham, Ksh. Nilakanta Singh, (2018). Determination of Optimal Number of Clusters in Cure Using Representative Points. International Journal of Computer Sciences and Engineering, 6(2), 313-320.

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Abstract

In most of the clustering algorithms, the number of clusters has to be supplied in as an input. In CURE clustering algorithm also, the same problem exists. In this paper, we try to find the optimal cluster number in the CURE clustering algorithm by calculating an optimality measure corresponding to each cluster number produced by CURE clustering algorithm after it enters a range ,based on the intra cluster measure and the inter cluster measure of the clusters. The clustering along with the optimality check continues as long the optimality measure is increasing and the cluster number doesn’t fall below the minimum boundary of our range.

Key-Words / Index Term

Algorithm, Clustering, CURE, Measure

References

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