POISSON DISTRIBUTION BASED INITIALIZATION FOR FUZZY CLUSTERING

被引：2
|
作者：
Vintr, Tomas [1 ]
Vintrova, Vanda [1 ]
Rezankova, Hana [1 ]
机构：
[1] Univ Econ, Fac Informat & Stat, Dept Probabil & Stat, Prague, Czech Republic
来源：
NEURAL NETWORK WORLD | 2012年 / 22卷 / 02期
关键词：
Poisson distribution based initialization; k-means; fuzzy c-means; possibilistic clustering algorithm; unsupervised possibilistic fuzzy clustering; ALGORITHM;
D O I：
10.14311/NNW.2012.22.009
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A quality of centroid-based clustering is highly dependent on initialization. In the article we propose initialization based on the probability of finding objects, which could represent individual clusters. We present results of experiments which compare the quality of clustering obtained by k-means algorithm and by selected methods for fuzzy clustering: FCM (fuzzy c-means), PCA (possibilistic clustering algorithm) and UPFC (unsupervised possibilistic fuzzy clustering) with different initializations. These experiments demonstrate an improvement in the quality of clustering when initialized by the proposed method. The concept how to estimate a ratio of added noise is also presented.
引用
收藏
页码：139 / 159
页数：21
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