A Survey on Clustering Algorithms
Clustering is a widely used technique to find interesting patterns dwelling in the dataset that remain unknown. In general, clustering is a method of dividing the data into groups of similar objects. One of significant research areas in data mining is to develop methods to modernize knowledge by using the existing knowledge, since it can generally augment mining efficiency,especially for very bulky database. Data mining uncovers hidden,previously unknown, and potentially useful information from large amounts of data. This paper presents a general survey of various clustering algorithms. In addition, the paper also describes the efficiency of Self-Organized Map (SOM) algorithm in enhancing the mixed data clustering.
Juha Vesanto and Esa Alhoniemi, “Clustering of Self-Organizing Map,” IEEE Transactions on Neural Networks, vol. 11, no. 3, May 2000, pp.586-600.
J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no.8, pp. 888-905, Aug. 2000.
Y. Gdalyahu, D. Weinshall, and M. Werman, “Self-Organization in Vision: Stochastic Clustering for Image Segmentation, Perceptual Grouping, and Image Database Organization,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 10, pp. 1053-1074, Oct. 2001.
J. C. Bezdek and S. K. Pal, Eds., “Fuzzy Models for Pattern Recognition: Methods that Search for Structures in Data,” New York:IEEE, 1992.
Mark Girolami, “Mercer Kernel-based Clustering in Feature space,”IEEE Transactions on Neural Networks, vol. 13, no. 3, May 2002.
Bernd Fischer, and J. M. Buhmann, “Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 4, April 2003.
B. Fischer, T. Zoller, and J.M. Buhmann, “Path Based Pair wise Data Clustering with Application to Texture Segmentation,” Energy Minimization Methods in Computer Vision and Pattern Recognition, pp. 235-250, LNCS 2134, 2001.
Bernd Fischer, and J. M. Buhmann, “Bagging for Path Based Clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 11, November 2003.
Leo Grady and Eric L. Schwartz, “Isoperimetric Graph Partitioning for Data Clustering and Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004.
Yaxin Bi, Sally McClean and Terry Anderson, “Improving Classification Decisions by Multiple Knowledge,” Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, 2005.
Zhijie Xu, Laisheng Wang, Jiancheng Luo and Jianqin Zhang, “A Modified Clustering Algorithm Data Mining,” IEEE 2005.
Massimiliano Pavan and Marcello Pelillo, “Dominant Sets and Pairwise Clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 1, January 2007.
M. Pavan and M. Pelillo, “Dominant Sets and Hierarchical Clustering,”Proceedings of IEEE International Conference Computer Vision, vol. 1,pp. 362-369, 2003.
M. Pavan and M. Pelillo, “Efficient Out-of-Sample Extension of Dominant-Set Clusters,” Advances in Neural Information Processing Systems 17,L.K. Saul, Y. Weiss, and L. Bottou, eds., pp. 1057-1064, 2005.
J. M. Buhmann, “Data Clustering and Learning,” Handbook of Brain Theory and Neural Networks, M. Arbib, ed., pp. 308-312, Bradfort Books/MIT Press, second ed., 2002.
A Tutorial on Clustering Algorithms,http://home .dei.polimi.it/matteucc/Clustering/tutorial_html.
Gautam Biswas, Jerry B. Weinberg, and Douglas H. Fisher, “ITERATE: A Conceptual Clustering Algorithm for Data Mining,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 28, part c, no. 2, pp. 100-111, 1998.
M. N. Vrahatis, B. Boutsinas, P. Alevizos, and G. Pavlides, “The New k- Windows Algorithm for Improving the k -Means Clustering Algorithm,” Journal of Complexity, Elsevier, vol. 18, no. 1, pp. 375-391, 2002.
Eman Abdu, and Douglas Salane, “A spectral-based clustering algorithm for categorical data using data summaries,” International Conference on Knowledge Discovery and Data Mining, ACM, Article no. 2, 2009.
Shijin Li, Jing Liu, Yuelong Zhu, and Xiaohua Zhang, “A New Supervised Clustering Algorithm for Data Set with Mixed Attributes,”Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, vol. 2, pp. 844-849, 2007.
Jian Yin, Zhi-Fang Tan, Jiang-Tao Ren, and Yi-Qun Chen, “An efficient clustering algorithm for mixed type attributes in large dataset,” Proceedings of 2005 International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1611-1614, 2005.
Tom Chiu, DongPing Fang, John Chen, Yao Wang, and Christopher Jeris, “A robust and scalable clustering algorithm for mixed type attributes in large database environment,” International Conference on Knowledge Discovery and Data Mining, pp. 263-268, 2001.
Mrutyunjaya Panda, and Manas Ranjan Patra, “Some Clustering Algorithms to Enhance the Performance of the Network Intrusion Detection System,” Journal of Theoretical and Applied Information Technology, pp. 710-716, 2008.
Qiang Li, Yan He, and Jing-ping Jiang, “A novel clustering algorithm based on quantum games,” Journal of Physics A: Mathematical and Theoritical, no. 44, 2009.
Jie Li, Xinbo Gao, and Li-cheng Jiao, “A GA-Based Clustering Algorithm for Large Data Sets with Mixed Numeric and Categorical Values,” Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications, IEEE Computer Society, p. 102, 2003.
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