A Survey on Clustering Analysis
Abstract
Cluster analysis is a collection of statistical methods, which identifies group of samples that react similarly or show similar characteristics. The simplest mechanism is to partition the samples using measurements that capture similarity or distance between samples. In this way, clusters and groups are interchangeable words. Often in research studies, cluster analysis is also referred as segmentation method. In neural network concepts, clustering method is called as unsupervised learning. Clustering is the subject of active research in several fields such as statistics, pattern recognition, and machine learning. This paper deals with the survey of cluster analysis.
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Pavel Berkhin,” Survey of Clustering Data Mining Techniques” Journal of Springer, (2006), pp. 25-71.
Rui Xu, Donald I. I. Wunsch ,” Survey of Clustering Algorithms” IEEE Transaction on neural networks, Vol.16, No.3, May 2005.pp. 645-678.
Sung-Hyuk Cha,” Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions” International Journal of mathematical models and methods in applied sciences, Volume 1, 2008, pp 91-103.
Pradeep Rai, Shubha Singh,”A survey of clustering techniques”, International journal of computer science,Vol.7, Oct 2010, pp 1-55.
Hruschka, E.R, Campello, R.J.G.B, Freitas, A.A, de Carvalho, A.C.P.L.F,”A Survey of Evolutionary Algorithms for Clustering”, IEEE Transaction on systems, man and cybernetics March 2009, pp 133 – 155.
Julie Scoltock,” A Survey of the Literature of ClusterAnalysis” Sub-Department of Industrial Management, The computer journal, Vol.25. No. 1 1981, pp 130-134
A.K. Jain, M.N.Mutyy and P.J.Flynn Data Clustering: A Review, ACM Computing Surveys, Vol. 31, No. 3, September 1999, pp 1-60
K.A. Abdul Nazeer, M.P.Sebastian, “Improving the Accuracy and Efficiency of the K-means Clustering Algorithm”, Proceeding of the World congress on Engineering 2009, Vol.1, July 2009.
A.K. Jane and R.C. Dubes, “Algorithm of clustering data”, Prentice Hall, Englewood Cliff, NJ, 1998
R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan, “Automatic subspace clustering of high dimensional data for data mining applications”, Proc. ACM SIGMOD international conference on Management of Data, pp. 94-105, 1998.
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