A Comparative Study between of Fuzzy C-Means Algorithms and Density based Spatial Clustering of Applications with Noise
Keywords:Data Clustering Algorithm, Data Mining, DBSCAN, FCM, Fuzzy C-Means, K-means
Data mining technology has emerged as a means of identifying patterns and trends from large amounts of data and is a computing intelligence area that provides tools for data analysis, new knowledge discovery, and autonomous decision making. Data clustering is an important problem in many areas. Fuzzy C-Means(FCM)[11,12,13] is a very important clustering technique based on fuzzy logic. DBSCAN(Density Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that is suitable for dealing with spatial data including noise and is a collection of arbitrary shapes and sizes. In this paper, we compare and analyze the performance of Fuzzy C-Means and DBSCAN algorithms in different data sets.
 A. Asuncion and D. J. Newman, UCI Machine Learning Repository Irvine, CA: University of California, School of Information and Computer Science, 2013.
 A. K. Jain, M. N. Murty and P. J. Flynn, â€œData Clustering: A reviewâ€, ACM Computing Surveys, vol. 31, no. 3, 1999.
 A. Rui and J. M. C. Sousa, â€œComparison of fuzzy clustering algorithms for Classificationâ€, International Symposium on Evolving Fuzzy Systems, 2006 , pp. 112-117.
 B. Jeon, Y. Yung and K. Hong â€Image segmentation by unsupervised sparse clustering, â€ pattern recognition letters 27science direct,(2006) 1650-1664
 H.P.K and l.M.P, â€œDensity-Based Clustering of Uncertain Dataâ€, KDD'05, August21-24, 2005, Chicago, Illinois, USA.
 J. C. Bezdek ,"Pattern Recognition with Fuzzy Objective Function Algoritms", Plenum Press, New York, 1981
 M. Ester, H. Kriegel, J. Sander, X. Xu, â€œ"A Density-Based Algorithm for Discovering
 Clusters in Large Spatial Databases with Noiseâ€", Proc. of ACM SIGMOD 3rd International
 Conference on Knowledge Discovery and Data Mining, pp. 226-231, AAAI Press, 1996.
 Richard J. Hathaway and James C. Bezdek, Extending Fuzzy and Probabilistic Clustering to Very Large Data Sets, Journal of Computational Statistics and Data Analysis, 2006, accepted.
 S. l Har-Peled and B. Sadri, "How fast is the k-means Method," in ACM-SIAM Symposium on Discrete Algorithms,Vancouver, 2005.
 Soumi Ghosh and Sanjay Kumar Dubey, â€œComparative Analysis of K-Means and Fuzzy C-Means Algorithmsâ€, IJACSA, Vol. 4, No.4, 2013,pp. 35-39.