Abstract

Clustering is one of the Data Mining tasks that can be used to cluster or group objects on the basis of their nearness to the central value. It has found many applications in the field of business, image processing, medical etc. K Means is one the method of clustering which is used widely because it is simple and efficient. The output of the K Means depends upon the chosen central values for clustering. So accuracy of the K Means algorithm depends much on the chosen central values. This paper presents the various methods evolved by researchers for finding initial clusters for K Means.

References

• Jiawei Han, Data mining: concepts and techniques (Morgan Kaufman Publishers, 2006).
• Margaret H Dunham, Data mining: introductory and advanced concepts (Pearson Education, 2006).
• Pena, J. M. , Lozano, J. A. , Larranaga, P, An empirical comparison of four initialization methods for the K-Means algorithm, Pattern Recognition Letters 20 (1999) pp. 1027-1040.
• Anderberg, M, Cluster analysis for applications (Academic Press, New York 1973).
• M. E. Celebi, H. Kingravi, P. A. Vela, A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm, Expert Systems with Applications, 40(1), 2013, pp. 200-210.
• Tou, J. , Gonzales, Pattern Recognition Principles (Addison-Wesley, Reading, MA, 1974).
• Katsavounidis, I. , Kuo, C. , Zhang, Z. , A new initialization technique for generalized lloyd iteration, IEEE Signal Processing Letters 1 (10), 1994, pp. 144-146.
• Takashi Onoda, Miho Sakai, Seiji Yamada, Careful Seeding Method based on Independent Components Analysis for k-means Clustering, Journal Of Emerging Technologies In Web Intelligence, vol. 4, No. 1, February 2012.
• Stephen J. Redmond, Conor Heneghan, A method for initialising the K-means clustering algorithm using kd-trees, Pattern Recognition Letters 28(8), 2007, pp. 965-973.
• Bradley, P. S. , Fayyad, Refining initial points for K-Means clustering: Proc. 15th International Conf. on Machine Learning, San Francisco, CA, 1998, pp. 91-99.
• Fernando Bacao, Victor Lobo, Marco Painho, Self-organizing maps as substitutes for K-means clustering, Computers and Geosciences, vol. 31, Elsevier, 2005, pp. 155-163.
• Khan, S. S. , Ahmad, A. , Cluster center initialization algorithm for k-means clustering, Pattern Recognition Letters 25 (11), 2004, pp. 1293-1302.
• Shehroz S. Khan, Shri Kant, Computation of initial modes for k-modes clustering algorithm using evidence accumulation, 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, 2007, pp. 2784-2789.
• Koheri Arai and Ali Ridho Barakbah, Hierarchical k-means: an algorithm for centroids initialization for k-means, Reports of The Faculty of Science and Engineering Saga University, vol. 36, No. 1, 2007.
• S. A. Majeed, H. Husain, S. A. Samad, A. Hussain, Hierarchical k-means algorithm applied on isolated Malay digit speech recognition, International Conference on System Engineering and Modeling, vol. 34, Singapore, 2012.
• Samarjeet Borah, M. K. Ghose, Performance Analysis of AIM-K-means & K- means in Quality Cluster Generation, Journal of Computing, vol. 1, Issue 1, December 2009.
• K. A. Abdul Nazeer and M. P. Sebastian, Improving the accuracy and efficiency of the k-means clustering algorithm, Proceedings of the World Congress on Engineering, London, UK, vol. 1, 2009.
• Madhu Yedla, S. R. Pathakota, T. M. Srinivasa, Enhancing K-means Clustering Algorithm with Improved Initial Centre, International Journal of Computer Science and Information Technologies, 1 (2) , 2010, pp. 121-125.