How to cite this paper
Alibuhtto, M & Mahat, N. (2020). Distance based k-means clustering algorithm for determining number of clusters for high dimensional data.Decision Science Letters , 9(1), 51-58.
Refrences
Alibuhtto, M.C., & Mahat, N.I. (2019). New approach for finding number of clusters using distance based k-means algorithm, International Journal of Engineering, Science and Mathematics, 8(4), 111-122.
Calinski, T., & Harabasz, J.(1974). A dendrite method for cluster analysis, Communications in Statistics, 3(1),1–27.
Dunn, J.C. (1974). Well separated clusters and optimal fuzzy partitions, Journal of Cybernetics, 4, 95-104.
Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and Techniques, San Francisco, CA, Litd: Morgan Kaufmann (Vol. 5).
Jain, A.K., & Dubes, R.C. (2011). Algorithms for Clustering Data. Pretice Hall, Englewood Cliffs, New Jersey.
Kameshwaran, K., & Malarvizhi, K. (2014). Survey on clustering techniques in data mining, International Journal of Computer Science and Information Technologies, 5(2), 2272–2276.
Kane, A., & Nagar, J. (2012). Determining the number of clusters for a k-means clustering algorithm. Indian Journal of Computer Science and Engineering (IJCSE), 3(5), 670–672.
Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An Introduction to Cluster Analysis. Wiley Series in Probability and Statistics. Eepe.Ethz.Ch.
Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining number of cluster in k-means clustering, International Journal of Advance Research in Computer Science and Management Studies, 1(6), 90–95.
Kumar, P., & Wasan, S. K. (2010). Comparative analysis of k-mean based algorithms, International Journal of Computer Science and Network Security, 10(4), 314–318.
Maulik, U., & Bandyopadhyay, S. (2002). Performance evaluation of some clustering algorithms and validity indices, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12), 1650-1654.
Mehar, A. M., Matawie, K., & Maeder, A. (2013). Determining an optimal value of k in k-means clustering, In Proceedings of the International Conference on Bioinformatics and Biomedicine: IEEE BIBM, 51–55.
Muca, M., & Kutrolli, G. (2015). A proposed algorithm for determining the optimal number of clusters. European Scientific Journal, 11(36), 112–120.
Ramageri, B.M. (2010). Data Mining Techniques and Applications, Indian Journal of Computer Science and Engineering, 1(4), 301-305.
Thakur, B., & Mann, M. (2014). Data mining for big data: A review, International Journal of Advanced Research in Computer Science and Software Engineering, 4(5), 469-473.
Visalakshi, N. K., & Suguna, J. (2009). K-means clustering using max-min distance measure, Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), 1–6.
Yadav, A., & Dhingra, S. (2016). A review on k-means clustering technique, International Journal of Latest Research in Science and Technology, 5(4), 13–16.
Calinski, T., & Harabasz, J.(1974). A dendrite method for cluster analysis, Communications in Statistics, 3(1),1–27.
Dunn, J.C. (1974). Well separated clusters and optimal fuzzy partitions, Journal of Cybernetics, 4, 95-104.
Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and Techniques, San Francisco, CA, Litd: Morgan Kaufmann (Vol. 5).
Jain, A.K., & Dubes, R.C. (2011). Algorithms for Clustering Data. Pretice Hall, Englewood Cliffs, New Jersey.
Kameshwaran, K., & Malarvizhi, K. (2014). Survey on clustering techniques in data mining, International Journal of Computer Science and Information Technologies, 5(2), 2272–2276.
Kane, A., & Nagar, J. (2012). Determining the number of clusters for a k-means clustering algorithm. Indian Journal of Computer Science and Engineering (IJCSE), 3(5), 670–672.
Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An Introduction to Cluster Analysis. Wiley Series in Probability and Statistics. Eepe.Ethz.Ch.
Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining number of cluster in k-means clustering, International Journal of Advance Research in Computer Science and Management Studies, 1(6), 90–95.
Kumar, P., & Wasan, S. K. (2010). Comparative analysis of k-mean based algorithms, International Journal of Computer Science and Network Security, 10(4), 314–318.
Maulik, U., & Bandyopadhyay, S. (2002). Performance evaluation of some clustering algorithms and validity indices, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12), 1650-1654.
Mehar, A. M., Matawie, K., & Maeder, A. (2013). Determining an optimal value of k in k-means clustering, In Proceedings of the International Conference on Bioinformatics and Biomedicine: IEEE BIBM, 51–55.
Muca, M., & Kutrolli, G. (2015). A proposed algorithm for determining the optimal number of clusters. European Scientific Journal, 11(36), 112–120.
Ramageri, B.M. (2010). Data Mining Techniques and Applications, Indian Journal of Computer Science and Engineering, 1(4), 301-305.
Thakur, B., & Mann, M. (2014). Data mining for big data: A review, International Journal of Advanced Research in Computer Science and Software Engineering, 4(5), 469-473.
Visalakshi, N. K., & Suguna, J. (2009). K-means clustering using max-min distance measure, Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), 1–6.
Yadav, A., & Dhingra, S. (2016). A review on k-means clustering technique, International Journal of Latest Research in Science and Technology, 5(4), 13–16.