References:
[1] Duan, G., Hu, W., and Zhang, Z. “A Novel Multilayer Data Clustering Framework based on Feature Selection and Modified K-Means Algorithm”, International Journal of Signal Processing, Image Processing and Pattern Recognition,9(4), pp. 81–90 (2016). http://dx.doi.org/10.14257/ijsip.2016.9.4.08.
[2] Haeri, A., & Tavakkoli-Moghaddam, R. “Developing a hybrid data mining approach based on multi-objective particle swarm optimization for solving a traveling salesman problem.” Journal of Business Economics and Management, 13(5), pp.951-967 (2012).
[3] Moslehi, F., Haeri, A., Moini, A. “Analyzing and Investigating the Use of Electronic Payment Tools in Iran using Data Mining Techniques.” Journal of AI and Data Mining, 6(2), pp.417-437 (2018). doi: 10.22044/jadm.2017.5352.1643.
[4] Amezquita-Sanchez, J.P., and Adeli, H. “Feature extraction and classification techniques for health monitoring of structures”, SCIENTIA IRANICA, 22(6), pp. 1931-1940 (2015).
[5] Cheng, T., Li, P., Zhu, S. and Torrieri, D. “M-cluster and X-ray: Two methods for multi jammer localization in wireless sensor networks”, Integrated Computer-Aided Engineering, 21(1), pp. 19-34 (2014).
[6] Gon_calves, N., Nikkila, J. and Vig_ario, R. “Selfsupervised mri tissue segmentation by discriminative clustering”, International Journal of Neural Systems, 24(1), 1450004 (16 pages) (2014).
[7] Saxena, A., Prasad, M., Gupta, A., and et al. “A review of clustering techniques and developments”, Neurocomputing, 267, pp. 664-681(2017).
[8] MacQueen, J.B. “Some methods for classification and analysis of multivariate observations”, Proc. 5-th Symp. Mathematical Statistics and Probability, Berkelely, CA 1967; 1:281–297.
[9] Huang, Z. “Extensions to the k-Means Algorithms for Clustering Large Data Sets with Categorical Values”, DATA MIN KNOWL DISC, 2, pp. 283-304 (1998).
[10] Green, P.E., Kim, J., and Carmone, F.J. “A preliminary study of optimal variable weighting in k-means clustering” Journal of Classification, 7(2), pp. 271-285 (1990).
[11] Huang, J.Z., Ng, M.K., Rong, H., and et al. “Automated variable weighting in k-means type clustering” IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5), pp. 657–668 (2005). https://doi.org/10.1109/TPAMI.2005.95.
[12] He, Z. “Evolutionary K-Means with pair-wise constraints”, Soft Computing, 20(1), pp. 287-301(2016).
[13] Yuan, F., Meng, Z.H., Zhangz, X., and Dong, C.R. “A New Algorithm to Get the Initial Centroids”, Proceedings of the 3rd International Conference on Machine Learning and Cybernetics, pp.26-29(2004).
[14] Zhang, C.h., Xia, S.h. “K-means Clustering Algorithm with Improved Initial center”, in Second International Workshop on Knowledge Discovery and Data Mining (WKDD), pp.790- 792 (2009).
[15] Nazeer, K.A.A., and Sebastian, M.P. “Improving the Accuracy and Efficiency of the k-means Clustering Algorithm” In Proceedings of the World congress on Engineering, 1, pp. 1–5 (2009).
[16] San, O.M., Huynh, V.N., and Nakamori, Y. “An alternative extension of the k-means algorithm for clustering categorical data” International Journal of Applied Mathematics and Computer Science, 14, pp.241-247(2004).
[17] Fahim, A.M., Salem, A.M., Torkey, F.A., and et al. “An efficient enhanced k-means clustering algorithm”, Journal of Zhejiang University-Science, 7(10), pp. 1626-1633 (2006).
[18] Ahmad, A. “k-mean clustering algorithm for mixed numeric and categorical data”, Data & Knowledge Engineering, 63 pp. 503–527 (2007). https://doi.org/10.1016/j.datak.2007.03.016.
[19] Arai, K., and Barakbah, A.R. “Hierarchical K-means: an algorithm for centroids initialization for K-means”, Reports of the Faculty of Science and Engineering, 36, pp. 25-31 (2007).
[20] Laszlo, M., Mukherjee, S.A. “genetic algorithm that exchanges neighboring centers for k-means clustering. Pattern Recognit. Lett, 28(16) pp. 2359–2366 (2007).
[21] Zalik, K.R. “An efficient k-means clustering algorithm”, Pattern Recognit. Lett, 29, pp.1385–1391 (2008).
[22] Kao, Y.T., Zahara, E., and Kao, I.W. “A hybridized approach to data clustering”, Expert Syst Appl, 34(3), pp. 1754–1762 (2008).
[23] Zhang, C.h., Xia, S.h. “K-means Clustering Algorithm with Improved Initial center”, in Second International Workshop on Knowledge Discovery and Data Mining (WKDD), pp.790- 792 (2009).
[24] Yedla, M., Pathakota, S.R, and Srinivasa, T.M. “Enhancing K-means clustering algorithm with improved initial center”, International Journal of computer science and information technologies, 1(2) pp.121-125 (2010).
[27] Niknama, T., Fard, E.T., Pourjafarian, N., and et al. “An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering”. ENG APPL ARTIF INTEL, 24(2) pp.306–317 (2011). https://doi.org/10.1016/j.engappai.2010.10.001.
[28] Hassanzadeh, T., and Meybodi, M.R. “A new hybrid approach for data clustering using firefly algorithm and K-means”, The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), (Aisp) 007–011. https://doi.org/10.1109/AISP.2012.6313708.
[29] Celebi, M.E, Kingravi, H.A, and Vela, P.A. “A comparative study of efficient initialization methods for the k-means clustering algorithm”, Expert Syst Appl. 40(1) pp. 200–210 (2013). https://doi.org/10.1016/j.eswa.2012.07.021.
[30] Tzortzis, G., and Likas, A. “The MinMax k-Means clustering algorithm”. Pattern Recognit, 47(7), pp.2505-2516 (2014).
[31] Guérin, J., Gibaru, O., Thiery, S., and Nyiri, E. “Clustering for Different Scales of Measurement-the Gap-Ratio Weighted K-means Algorithm”, arXiv preprint arXiv pp. 1703.07625 2017.
[32] Lin, K.P. “Privacy-preserving kernel k-means clustering outsourcing with random transformation,” Knowledge and Information Systems, pp. 1–24 (2016).
[33] Nagwani, N. K., and Sharaff, A. “SMS spam filtering and thread identification using bi-level text classification and clustering techniques”, Journal of Information Science, 43(1), pp. 75-87 (2017).
[34] Chen, L., Xu, Z., Wang, H., and Liu, S. “An ordered clustering algorithm based on K-means and the PROMETHEE method”, International Journal of Machine Learning and Cybernetics, 9(6) pp. 917-926 (2018).
[35] Gan, G., and Ng, M. K. P. “k-means clustering with outlier removal.”,Pattern Recognition Letters, 90, pp. 8-14 (2017).
[36] Yaghini, M., & Ghazanfari, N. “Tabu-KM: a hybrid clustering algorithm based on tabu search approach”, International Journal of Industrial Engineering & Production Research, 21(2) pp.71-79 (2010).
[37] Han, J., Kamber, M., and Tung, A. KH. “Spatial clustering methods in data mining: A survey”, London: Taylor & Francis (2001).
[38] Jain, A.K., Murty, M.N., and Flynn, P.J. “Data clustering: A review”, ACM Computing Surveys 1999; 31, pp. 264–323 (1999).
[39] Maimon, O.Z., and Rokach, L. “Data mining and knowledge discovery handbook”, New York: Springer (2005).
[40] FAZEL, Z. M., & Zarinbal, M. “Image Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach”, Journal of Industrial Engineering & Production Research, 23(4) pp. 245-251(2012).
[41] Farajian, M. A., & Mohammadi, S. “Mining the banking customer behavior using clustering and association rules methods”, Journal of Industrial Engineering & Production Research, 21(4) pp.239-245 (2010).