Determination of Optimal Number of Clusters in Cure Using Representative Points

Authors

  • Robindro K Department of Computer Science, Manipur University, Canchipur, Imphal, Manipur, India
  • Khumukcham B Department of Computer Science, Manipur University, Canchipur, Imphal, Manipur, India
  • Ksh. Nilakanta Singh Department of Computer Science, Manipur University, Canchipur, Imphal, Manipur, India

DOI:

https://doi.org/10.26438/ijcse/v6i2.313320

Keywords:

Algorithm, Clustering, CURE, Measure

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.

References

Thomas H. Cormen, Charles E. Leiserson, and Ronald L. Rivest. Introduction to Algorithms, The MIT Press, Massachusetts, 1990.

Hanan Samet. The Design and Analysis of Spatial Data Structures. Addison-Wesley Publishing Company Inc., New York, 1990.

R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, second ed., Wiley-Interscience, New York, 2001.

J.Han, M. Kamber, Data Mining, Morgan Kaufmann Publishers, 2000.

M. Grotschel, L. Lovasz, A. Schrijver, Geometric Algorithms and Combinatorial Optimization, second ed., Algorithm and Combinatorics, vol. 2, Springer-Verlag, Berlin, Heidelberg, 1994.

A.K. Jain, M.N. Murty, P.J. Flynn, Data clustering: A review, ACM Computing Surveys 31 (1999) 264–323.

V.V. Vazirani, Approximation Algorithms, Springer, 2001.

Sudipto Guha, Rajeev Rastogi, and Kyuseok Shim (1998) CURE: An Efficient Clustering Algorithm for Large Databases

Siddheswar Ray and Rose H. Turi Determination of Number of Clusters in K-Means Clustering and Application in Colour Image Segmentation.

Ng R., Han J. (1994) Efficient and effective clustering method for spatial data mining. In Proc. Conf. on Very Large Data Bases, pp 144-155.

Ester M., Kriegel H., Sander J., and Xu X. (1996) A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proc. 3rd Int. Conf. on Knowledge Discovery and Data Mining, AAAI Press.

Hinneburg A., Keim D. (1998) An Efficient Approach to Clustering in Large Multimedia Databases with Noise. Proc AAAI.

Seikholeslami G., Chatterjee S., and Zhang A. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. Proceedings of the 24th VLDB Conference, 1998

Guha, S., R. Rastogi & K. Shim. CURE: An Efficient Clustering Algorithm for Large Databases. In Proc. Of ACM SIGMOD Intl. Conf. on Management of Data, pp. 73-82, 1998.

Karypis, G., E. Han & V. Kumar. Chameleon: A hierarchical clustering algorithm using dynamic modeling. IEEE Computer, 32(8) pp. 68-75, 1999.

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Published

2025-11-12
CITATION
DOI: 10.26438/ijcse/v6i2.313320
Published: 2025-11-12

How to Cite

[1]
K. Robindro, B. Khumukcham, and K. Nilakanta Singh, “Determination of Optimal Number of Clusters in Cure Using Representative Points”, Int. J. Comp. Sci. Eng., vol. 6, no. 2, pp. 313–320, Nov. 2025.

Issue

Vol. 6 No. 2 (2018): IJCSE February Edition

Section

Research Article

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