Abstract

Clustering in data mining is very important to discover distribution patterns and this importance tends to increase as the amount of data grows. It is one of the main analytical methods in data mining and its method influences its results directly. K-means is a typical clustering algorithm[3]. It mainly consists of two phases i.e. initializing random clusters and to find the nearest neighbour. Both phases have some shortcomings which are discussed in the paper and two methods are purposed based on that. First one is about how to generate the centroids and the second one will reduce the time while calculating distance from centroid.

References

• Rajeswari, K., Acharya, O., Sharma, M., Kopnar, M., & Karandikar, K.” Improvement in k-Means Clustering Algorithm Using Data Clustering”, In Computing Communication Control and Automation (ICCUBEA), 2015 International Conference on,vol.3, no.15, pp. 367-369, IEEE.
• Research on k-means Clustering Algorithm An Improved k-means Clustering Algorithm Shi Na College of Information Engineering, Capital Normal
• Lima, M. F., Zarpelao, B. B., Sampaio, L. D., Rodrigues, J. J., Abrao, T., & Proença Jr, M. L.” Detection using baseline and K-means clustering”, In Software, Telecommunications and Computer Networks (softcom), 2010 International Conference on, vol.3, no.5 pp. 305-309, IEEE.
• Ren, Q., & Zhuo, X. “ Application of an improved k-means algorithm in gene expression data analysis” In Systems Biology (ISB), 2011 International Conference on, pp. 87-91, IEEE.
• Wang, H., Qi, J., Zheng, W., & Wang, M. “Balance K-means algorithm. In Computational Intelligence and Software Engineering,” Cise 2009 International Conference on, pp. 1-3, IEEE.
• Esteves, R. M., Hacker, T., & Rong, C. “Competitive k-means, a new accurate and distributed k-means algorithm for large datasets” In Cloud Computing Technology and Science (cloudcom), 2013 IEEE 5th International Conference on ,Vol. 1, pp. 17-24, IEEE.
• Tian, L., & Jianwen, W. “Research on network intrusion detection system based on improved k-means clustering algorithm”, In computer Science-Technology and Applications, 2009. IFCSTA'09. International Forum on Vol. 1, pp. 76-79, IEEE.
• Singh, G., Antony, D. A., & Leavline, E. J” Data mining in network security-techniques & tools: a research perspective”, Journal of theoretical & applied information technology, vol.2, no.57
• Yang, Q., & Wu, X. “10 challenging problems in data mining research.International Journal of Information Technology & Decision Making”, vol.5, no.4,pp.597-604.
• Chen, C. H., Tseng, V. S., & Hong, T. P. ,”Cluster-based evaluation in fuzzy-genetic data mining. Fuzzy Systems”, IEEE Transactions on, vol. 1, no.16,pp. 249-262.
• Liao, S. H., Chu, P. H., & Hsiao, P. Y,” Data mining techniques and applications–A decade review from 2000 to 2011”, Expert Systems with Applications, vol.12, no.39, pp.11303-11311.
• Balabantaray, R. C., Sarma, C., & Jha, M. (2015). Document Clustering using K-Means and K- Medoids. Arxiv preprint arxiv:1502.07938.
• Sujatha, M. S., & Sona, M. A. S.,”New fast k-means clustering algorithm using modified centroid selection method”, Ininternational Journal of Engineering Research and Technology ,Vol. 2, No. 2 ,February-2013.
• Brar, R., & Sharma, N., “A Novel Density Based K-Means Clustering Algorithm for Intrusion Detection”, Journal of Network Communications and Emerging Technologies (JNCET) www. Jncet. Org, vol.3, no.7
• W. Zhao, H. Ma, and Q. He, “Parallel K-Means Clustering Based on MapReduce,” vol. 5931, Springer Berlin / Heidelberg, 2009, pp. 674– 679.
• B. Bahmani, B. Moseley, A. Vattani, R. Kumar, and S. Vassilvitskii, “Scalable k-means++,” Proc. VLDB Endow., vol. 5, no. 7, pp. 622–633, 2012.
• M.V.B.T.Santhi,V.R.N.S.S.V.SaiLeela,P.U.Anitha,D.Nagamalleswari” Enhancing K-Means Clustering Algorithm” International Journal on Computer Science And Technology(IJCST) Vol 2,Issue 4,Oct-Dec 2011