Document Type : Research Article
Authors
^{1} Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
^{2} Dept. of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
^{3} Dept. of Decision Science and Knowledge Engineering, University of Economic Sciences, Tehran, Iran
Abstract
Highlights
[1] R. Duwairi, M. Abu-Rahmeh, A novel approach for initializing the spherical K-means clustering algorithm, Simulation Modelling Practice and Theory, 54 (2015) 49-63.
[2] H. Mashayekhi, J. Habibi, S. Voulgaris, M. van Steen, GoSCAN: Decentralized scalable data clustering, Computing, 95(9) (2013) 759-784.
[3] S.M.R. Zadegan, M. Mirzaie, F. Sadoughi, Ranked k-medoids: A fast and accurate rank-based partitioning algorithm for clustering large datasets, Knowledge-Based Systems, 39 (2013) 133-143.
[4] J. MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Oakland, CA, USA., 1967, pp. 281-297.
[5] H.-S. Park, C.-H. Jun, A simple and fast algorithm for K-medoids clustering, Expert systems with applications, 36(2) (2009) 3336-3341.
[6] J.C. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, (1973).
[7] S. Miyamoto, K. Umayahara, Methods in hard and fuzzy clustering, in: Soft computing and human-centered machines, Springer, 2000, pp. 85-129.
[8] T. Johnson, S.K. Singh, Genetic algorithms based enhanced K Strange points clustering algorithm, in: Computing and Network Communications (CoCoNet), 2015 International Conference on, IEEE, 2015, pp. 737-741.
[9] A. Likas, N. Vlassis, J.J. Verbeek, The global k-means clustering algorithm, Pattern recognition, 36(2) (2003) 451-461.
[10] D. Arthur, S. Vassilvitskii, k-means++: The advantages of careful seeding, in: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, 2007, pp. 1027-1035.
[11] C. Zhang, D. Ouyang, J. Ning, An artificial bee colony approach for clustering, Expert Systems with Applications, 37(7) (2010) 4761-4767.
[12] W. Kwedlo, A clustering method combining differential evolution with the K-means algorithm, Pattern Recognition Letters, 32(12) (2011) 1613-1621.
[13] T. Kohonen, The self-organizing map, Proceedings of the IEEE, 78(9) (1990) 1464-1480.
[14] S. Wu, T.W. Chow, Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density, Pattern Recognition, 37(2) (2004) 175-188.
[15] Y. Dogan, D. Birant, A. Kut, SOM++: integration of self-organizing map and k-means++ algorithms, in: International Workshop on Machine Learning and Data Mining in Pattern Recognition, Springer, 2013, pp. 246-259.
[16] A. Neme, S. Hernández, O. Neme, L. Hernández, Self-Organizing Maps with Non-cooperative Strategies (SOM-NC), in: WSOM, Springer, 2009, pp. 200-208.
[17] A.P. Engelbrecht, Computational intelligence: an introduction, John Wiley & Sons, 2007.
[18] L. Pavel, Game theory for control of optical networks, Springer Science & Business Media, 2012.
[19] J. Shen, S.I. Chang, E.S. Lee, Y. Deng, S.J. Brown, Determination of cluster number in clustering microarray data, Applied Mathematics and Computation, 169(2) (2005) 1172-1185.
[20] S. Subramani, S. Balasubramaniam, Post mining of diversified multiple decision trees for actionable knowledge discovery, in: International Conference on Advanced Computing, Networking and Security, Springer, 2011, pp. 179-187.
[21] T. Martinetz, K. Schulten, A" neural-gas" network learns topologies, (1991).
[22] B. Bahmani, B. Moseley, A. Vattani, R. Kumar, S. Vassilvitskii, Scalable k-means++, Proceedings of the VLDB Endowment, 5(7) (2012) 622-633.
[23] http://cs.uef.fi/sipu/datasets.
[24] https://archive.ics.uci.edu/ml/datasets.
Keywords