Evaluation of Data Mining Strategies Using Fuzzy Clustering in Dynamic Environment

  • Chatti Subbalakshmi
  • G. Ramakrishna
  • S. Krishna Mohan Rao
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 44)

Abstract

The recent applications of data mining such as biological, scientific, financial and others are changing data regularly, which is uncertain and incomplete. For finding tendency in these data up-to-date, we need to modify existing data mining algorithms with dynamic characteristics. Soft computing methods are suitable for finding changes in uncertain data. In order to adopt change in data we can apply any of two approaches, update algorithm by ignoring earlier state or update with respect to earlier state. In this paper, we have framed two fuzzy clustering methods based on these approaches and implementation done using R software with comparison.

Keywords

Data mining strategy Changing data Fuzzy c-means Silhouette index 

References

  1. 1.
    Hartigan, J.A. Clustering Algorithms. Wiley, New York (1975)Google Scholar
  2. 2.
    Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a K-Means clustering algorithm. J. Roy. Stat. Soc. Ser. C 28(1), 100–108 (1979)MATHGoogle Scholar
  3. 3.
    Kaufman, L., Rousseau, P.J.: Clustering by means of medoids. In: Dodge, Y. (ed.) Statistical Data Analysis Based on the L1–Norm and Related Methods, pp. 405–416. North-Holland, Amsterdam (1987)Google Scholar
  4. 4.
    Park, H.S., Jun, C.H.: A simple and fast algorithm for K-medoids clustering. Expert Syst. Appl. 36(2), 3336–3341 (2009)CrossRefGoogle Scholar
  5. 5.
    Crespo, F., Weber, R.: A methodology for dynamic data mining based on fuzzy clustering. Fuzzy Sets Syst. 150(2), 1 (2005)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Peters, G., Weber, R., Nowatzke, R.: Dynamic rough clustering and its applications. J. Appl. Soft Comput. 12(2012), 3193–3207 (2012)CrossRefGoogle Scholar
  7. 7.
    Peters, G., Weber, R.: Intelligent cluster algorithms for changing data structures. Int. J. Intell. Def. Syst. 2(2), 105–119 (2009)Google Scholar
  8. 8.
    Nock, R., Nielsen, F.: On weighting clustering. IEEE Trans. Pattern Anal. Mach. Intell. 28(8), 1–13 (2006)Google Scholar
  9. 9.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981). ISBN 0-306-40671-3Google Scholar
  10. 10.
    Visvanathan, M., Adagarla, B.S., Gerald, H.L., Smith, P.: Cluster validation: an integrative method for cluster analysis. In: IEEE International Conference BIBMW, pp. 238–242 (2009)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Chatti Subbalakshmi
    • 1
  • G. Ramakrishna
    • 2
  • S. Krishna Mohan Rao
    • 3
  1. 1.Guru Nanak Institutions Technical CampusHyderabadIndia
  2. 2.K L UniversityVijayawadaIndia
  3. 3.Siddhartha Engineering CollegeHyderabadIndia

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