In digital era large volumes of data are generated by enterprises. Mining on this large volume of data provides valuable insights into user behaviors and helps to improve the business. Various Machine learning algorithms are proposed for data mining. Clustering is an important data mining algorithm for grouping the records and analyzing the data. K-means is a most used Clustering algorithm, but the time taken to cluster large volume of records is high. To reduce the clustering time many approaches are proposed in literature. In this work an improved K-means clustering is proposed which is able to reduce the clustering time.

K-means, Clustering, Centroids

[1] Wang Shunye “An Improved K-means Clustering Algorithm Based on Dissimilarity” 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)Dec 20-22, 2013, Shenyang, China IEEE.
[2] Navjot Kaur, Jaspreet Kaur Sahiwal, Navneet Kaur “EFFICIENT KMEANSCLUSTERING ALGORITHM USING RANKING METHOD IN DATA MINING” ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 3, May2012.
[3] Md. Sohrab Mahmud, Md. Mostafizer Rahman, and Md.Nasim Akhtar ―”Improvement of K-means Clustering algorithm with better initial centroids based on weighted average” 2012 7th International Conference on Electrical and Computer Engineering 20-22 December, 2012, Dhaka, Bangladesh, 2012 IEEE.
[4] Juntao Wang & Xiaolong Su “ An improved K-Means clustering algorithm” 2011 IEEE.
[5] Mohamed Abubaker, Wesam Ashour, "Efficient Data Clustering Algorithms: Improvements over K-means", International Journal of Intelligent Systems and Applications, vol. 5, issue 3, pages 37-49, 2013.
[6] Mohammed EI Agha, Wesam M. Ashour, " Efficient and Fast Initializtion Algorithm for K-means Clustering", LJ. Intelligent Systems and Applications, vol. 4, issue 1, pages 21-31, 2012
[7] Stephen J. Redmon, Conor Heneghan, " A method for initializing the K-means clustering algorithm using kd-trees", Journal Pattern Recognition Letters, vol. 28, issue 8, pages 965-973, 2007.
[8] Ling-bo Han, Qiang Wang, Zhengfeng Jiang etc..Improved k-means initial clustering center selection algorithm. Computer Engineering and Applications. 2010, 46(17):150–152.
[9] 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
[10] Idrizi F., Rustemi, A., & Dalipi F., (2017, June), Anew modified sorting algorithm: A comparison with state of the art. In embedded computing (MECO) .20176th Mediterranean Conference on (pp 1-6)IEEE.
[11] 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.
[12]MerzCand Murphy P, UCI Repository of MachineLearningDatabases,Available:ftp://ftp.ics.uci.edu/pub/machine-learning-databases