Improved fuzzy art method for initializing K-means

被引：0

作者：

Ilhan S. ^{[1
]}

Duru N. ^{[1
]}

Adali E. ^{[2
]}

机构：

[1] Computer Engineering Department, Kocaeli University, Kocaeli, Umuttepe Campus

[2] Computer Engineering Department, Istanbul Technical University, Istanbul, Ayazaga Campus

来源：

International Journal of Computational Intelligence Systems | 2010年 / 3卷 / 3期

关键词：

Clustering; Improved fuzzy ART method; Initial center determination; K-means clustering;

D O I：

10.2991/ijcis.2010.3.3.3

中图分类号：

学科分类号：

摘要：

The K-means algorithm is quite sensitive to the cluster centers selected initially and can perform different clusterings depending on these initialization conditions. Within the scope of this study, a new method based on the Fuzzy ART algorithm which is called Improved Fuzzy ART (IFART) is used in the determination of initial cluster centers. By using IFART, better quality clusters are achieved than Fuzzy ART do and also IFART is as good as Fuzzy ART about capable of fast clustering and capability on large scaled data clustering. Consequently, it is observed that, with the proposed method, the clustering operation is completed in fewer steps, that it is performed in a more stable manner by fixing the initialization points and that it is completed with a smaller error margin compared with the conventional K-means.

引用

页码：274 / 279

页数：5

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