Advertisement

A Novel Clustering Algorithm Based on Variable Precision Rough-Fuzzy Sets

  • Zhiqiang Bao
  • Bing Han
  • Shunjun Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

In the field of cluster analysis and data mining, fuzzy c-means algorithm is one of effective methods, which has widely used in unsupervised pattern classification. However, the above algorithm assumes that each feature of the samples plays a uniform contribution for cluster analysis. To consider the different contribution of each dimensional feature of the given samples to be classified, this paper presents a novel fuzzy c-means clustering algorithm based on feature weighted, in which the Variable Precision Rough-Fuzzy Sets is used to assign the weights to each feature. Due to the advantages of Rough Sets for feature reduction, we can obtain the better results than the traditional one, which enriches the theory of FCM-type algorithms. Then, we apply the proposed method into video data to detect shot boundary in video indexing and browsing. The test experiment with UCI data and the video data from CCTV demonstrate the effectiveness of the novel algorithm.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    He, Q.: Advance of the Theory and Application of Fuzzy Clustering Analysis. Fuzzy System and Fuzzy Mathematics 12(2), 89–94 (1998) (In Chinese)Google Scholar
  2. 2.
    Huang, Z.X., Michael, K.N.: A Fuzzy K-modes Algorithm for Clustering Categorical Data. IEEE Transactions on Fuzzy Systems 7(4), 446–452 (1999)CrossRefGoogle Scholar
  3. 3.
    Pawlak, Z.: Rough Set. International Journal of Computer and Information Science 11(5), 341–356 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Dubois, D., Prade, H.: Rough fuzzy Sets and Fuzzy Rough Sets. International Journal of General Systems 17, 191–209 (1990)zbMATHCrossRefGoogle Scholar
  5. 5.
    Gao, X.B., Han, B., Ji, H.G.: Shot Boundary Detection Method for News Video Based on Rough Sets and Fuzzy Clustering. In: Kamel, M.S., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 231–238. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Xiao, X.B., Tang, O.: Unsupervised Model-free News Video Segmentation. IEEE Trans. on Circuits and Systems for Video Technology 12(9), 765–776 (2002)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Zhang, H.J.: Automatic Partitioning of Full Motion Video. Multimedia Systems 1(1), 10–28 (1993)CrossRefGoogle Scholar
  8. 8.
    Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. New York (1973)Google Scholar
  9. 9.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Object Function Algorithms. Plenum, New York (1981)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhiqiang Bao
    • 1
  • Bing Han
    • 2
  • Shunjun Wu
    • 1
  1. 1.Key Lab of Radar Signal ProcessingXidian UniversityXi’anChina
  2. 2.School of Electronic EngineeringXidian Univ.Xi’anChina

Personalised recommendations