Cluster Analysis Algorithms Based on the Gradient Descent Procedure of a Fuzzy Objective Function

  • Rhee, Hyun-Sook (Department of Computer Science and Management Technology, Dongyang Technology Junior College) ;
  • Oh, Kyung-Whan (Department of Computer Science, Sogang University)
  • 발행 : 1997.12.01

초록

Fuzzy clustering has been playing an important role in solving many problems. Fuzzy c-Means(FCM) algorithm is most frequently used for fuzzy clustering. But some fixed point of FCM algorithm, know as Tucker's counter example, is not a reasonable solution. Moreover, FCM algorithm is impossible to perform the on-line learning since it is basically a batch learning scheme. This paper presents unsupervised learning networks as an attempt to improve shortcomings of the conventional clustering algorithm. This model integrates optimization function of FCM algorithm into unsupervised learning networks. The learning rule of the proposed scheme is a result of formal derivation based on the gradient descent procedure of a fuzzy objective function. Using the result of formal derivation, two algorithms of fuzzy cluster analysis, the batch learning version and on-line learning version, are devised. They are tested on several data sets and compared with FCM. The experimental results show that the proposed algorithms find out the reasonable solution on Tucker's counter example.

키워드

참고문헌

  1. Pattern Classification and Scene Analysis R.O.Duda;P.E.Hart
  2. Pattern Recognition v.27 no.2 Clustering with Evolution Strategies Babu,G.P.;Murty,M.N.
  3. Self-Organizing Maps Berlin T.Kohonen
  4. Pattern Recognition with fuzzy Objective Function Algorithms, J.C.Bezdek
  5. Proc. 1994 Int. Conf. on System, Man and Cybemetics A Fuzzy Algorithm for learning Vector Quantization N.B.Karaiannis;P.I.Pai PI
  6. Pattern Recognition v.27 no.5 Fuzzy Kohonen Clustering Networks E.C.Tsao;J.C.Bezdek;N.R.Pal
  7. Int'1, J. Ceneral Systems v.16 no.4 Parallel Self-Organizing Feature Maps for Unsupervised Pattern Recognition Huntsberge,T.L.
  8. Information and Control v.8 Fuzzy Sets L.A.Zadeh
  9. Pattern Recognition v.18 no.2 Iterative Fuzzy Image Segmentation T.L.Huntsberger;C.L.Jacobs;R.L.Cannon
  10. IEEE Trans. on Pattern Analysis and Machine Intelligence v.PAMI-8 no.2 Efficient Implementation of the Fuzzy c-Means Clustering Algorithms R.L.Cannon;J.V.Dave;J.C.Bezdek
  11. IEEE Trans. Syst. Man. Cybern. v.17 no.5 Convergence Theory for Fuzzy c-Means: Counter examples and Repairs J.C.Bezdek;R.J.Hathaway;M.J.Sabin:W.T.Tucker