Convergence Theory for Generalized Possibilistic Clustering Algorithms
A generalized approach to possibilistic clustering algorithms was proposed in 18, where the memberships are evaluated directly according to the data information using the fuzzy set theory, and the cluster centers are updated via a performance index. The computational experiments based on the generalized possibilistic clustering algorithms in 18 revealed that these clustering algorithms could not provide very stable results when clustering some data sets. As a further investigation on the generalized possibilistic clustering algorithms, this paper discusses the convergence theory in the algorithms and proves that all the generalized possibilistic clustering algorithms are convergent to the local minimum of the objective functions.
Fuzzy clustering possibilistic clustering convergence theory Picard iteration
Qihang Lin Jian Zhou Xin Zheng
Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
国际会议
江西庐山
英文
71-79
2007-10-10（万方平台首次上网日期，不代表论文的发表时间）