A Comparative Study between of Fuzzy C-Means Algorithms and Density based Spatial Clustering of Applications with Noise

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    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)[8] 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.

     

     


  • Keywords


    Data Clustering Algorithm, Data Mining, DBSCAN, FCM, Fuzzy C-Means, K-means

  • References


      [1] A. Asuncion and D. J. Newman, UCI Machine Learning Repository Irvine, CA: University of California, School of Information and Computer Science, 2013.

      [2] A. K. Jain, M. N. Murty and P. J. Flynn, “Data Clustering: A review”, ACM Computing Surveys, vol. 31, no. 3, 1999.

      [3] A. Rui and J. M. C. Sousa, “Comparison of fuzzy clustering algorithms for Classification”, International Symposium on Evolving Fuzzy Systems, 2006 , pp. 112-117.

      [4] B. Jeon, Y. Yung and K. Hong ”Image segmentation by unsupervised sparse clustering, ” pattern recognition letters 27science direct,(2006) 1650-1664

      [5] https://en.wikipedia.org/wiki/DBSCAN#Complexity

      [6] H.P.K and l.M.P, “Density-Based Clustering of Uncertain Data”, KDD'05, August21-24, 2005, Chicago, Illinois, USA.

      [7] J. C. Bezdek ,"Pattern Recognition with Fuzzy Objective Function Algoritms", Plenum Press, New York, 1981

      [8] M. Ester, H. Kriegel, J. Sander, X. Xu, “"A Density-Based Algorithm for Discovering

      [9] Clusters in Large Spatial Databases with Noise”", Proc. of ACM SIGMOD 3rd International

      [10] Conference on Knowledge Discovery and Data Mining, pp. 226-231, AAAI Press, 1996.

      [11] 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.

      [12] S. l Har-Peled and B. Sadri, "How fast is the k-means Method," in ACM-SIAM Symposium on Discrete Algorithms,Vancouver, 2005.

      [13] Soumi Ghosh and Sanjay Kumar Dubey, “Comparative Analysis of K-Means and Fuzzy C-Means Algorithms”, IJACSA, Vol. 4, No.4, 2013,pp. 35-39.


 

View

Download

Article ID: 18592
 
DOI: 10.14419/ijet.v7i3.33.18592




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.