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A Fast K-means and Fuzzy-c-means Algorithms using Adaptively Initialization  

강지혜 (충북대학교 전기공학과)
김성수 (충북대학교 전기공학과)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.4, 2004 , pp. 516-524 More about this Journal
Abstract
In this paper, the initial value problem in clustering using K-means or Fuzzy-c-means is considered to reduce the number of iterations. Conventionally the initial values in clustering using K-means or Fuzzy-c-means are chosen randomly, which sometimes brings the results that the process of clustering converges to undesired center points. The choice of intial value has been one of the well-known subjects to be solved. The system of clustering using K-means or Fuzzy-c-means is sensitive to the choice of intial values. As an approach to the problem, the uniform partitioning method is employed to extract the optimal initial point for each clustering of data. Experimental results are presented to demonstrate the superiority of the proposed method, which reduces the number of iterations for the central points of clustering groups.
Keywords
K-means and Fuzzy-c-means Clustering; Initial Value; Uniform partitioning;
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