Advertisement

A Novel Clustering Algorithm Based on Gravity and Cluster Merging

  • Jiang Zhong
  • Longhai Liu
  • Zhiguo Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6440)

Abstract

Fuzzy C-means (FCM) clustering algorithm is commonly used in data mining tasks. It has the advantage of producing good modeling results in many cases. However, it is sensitive to outliers and the initial cluster centers. In addition, it could not get the accurate cluster number during the algorithm. To overcome the above problems, a novel FCM algorithm based on gravity and cluster merging was presented in this paper. By using gravity in this algorithm, the influence of outliers was minimized and the initial cluster centers were selected. And by using cluster merging, an appropriate number of clustering could be specified. The experimental evaluation shows that the modified method can effectively improve the clustering performance.

Keywords

Fuzzy C-means Algorithm Gravity Cluster Merge 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dunn, J.C.: A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics 3, 32–57 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)CrossRefzbMATHGoogle Scholar
  3. 3.
    Davé, R.N.: Characterization and detection of noise in clustering. Pattern Recognition Letters 12, 657–664 (1991)CrossRefGoogle Scholar
  4. 4.
    Frigui, H., Krishnapuram, R.: Clustering by competitive agglomeration. Pattern Recognition 30, 1109–1119 (1997)CrossRefGoogle Scholar
  5. 5.
    Sun, H.J., Wang, S.R., Jiang, Q.H.: FCM-Based Model Selection Algorithms for Determining the Number of Clusters. Pattern Recognition 37, 2027–2037 (2004)CrossRefzbMATHGoogle Scholar
  6. 6.
    Indulska, M., Orlowska, M.E.: Gravity Based Spatial Clustering. In: Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems, pp. 125–130. Year of Publication, Virginia (2002)Google Scholar
  7. 7.
    Frossyniotis, D., Pertselakis, M., Stafylopatis, A.: A Multi-Clustering Fusion Algorithm. In: Proceedings of the Second Hellenic Conference on Artificial intelligence, pp. 225–236. Year of Publication, Thessaloniki (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jiang Zhong
    • 1
  • Longhai Liu
    • 1
  • Zhiguo Li
    • 2
  1. 1.College of Computer ScienceChongqing UniversityChongqingChina
  2. 2.Shanghai Baosight Software CorporationChongqingChina

Personalised recommendations