Hierarchical unsupervised fuzzy clustering

被引：42
|
作者：
Geva, AB [1 ]
机构：
[1] Ben Gurion Univ Negev, Dept Elect & Comp Engn, IL-84105 Beer Sheva, Israel
来源：
IEEE TRANSACTIONS ON FUZZY SYSTEMS | 1999年 / 7卷 / 06期
基金：
以色列科学基金会;
关键词：
cluster validity; hierarchical clustering; hybrid systems; pattern recognition; projection pursuit; recursive feature extraction; unsupervised fuzzy clustering;
D O I：
10.1109/91.811242
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A new recursive algorithm for hierarchical fuzzy partitioning is presented. The algorithm has the advantages of hierarchical clustering, while maintaining fuzzy clustering rules. Each pattern can have a nonzero membership in more than one subset of the data in the hierarchy. Optimal feature extraction and reduction is optionally reapplied for each subset. Combining hierarchical and fuzzy concepts is suggested as a natural feasible solution to the cluster validity problem of real data. The convergence and membership conservation of the algorithm are proven. The algorithm is shown to be effective for a variety of data sets with a wide dynamic range of both covariance matrices and number of members in each class.
引用
收藏
页码：723 / 733
页数：11
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