On Fuzzy Clustering of Data Streams with Concept Drift

被引：0

作者：

Jaworski, Maciej ^{[1
]}

Duda, Piotr ^{[1
]}

Pietruczuk, Lena ^{[1
]}

机构：

[1] Czestochowa Tech Univ, Dept Comp Engn, PL-42200 Czestochowa, Poland

来源：

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT II | 2012年 / 7268卷

关键词：

IDENTIFICATION; ALGORITHMS; SYSTEMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the paper the clustering algorithms based on fuzzy set theory are considered. Modifications of the Fuzzy C-Means and the Possibilistic C-Means algorithms are presented, which adjust them to deal with data streams. Since data stream is of infinite size, it has to be partitioned into chunks. Simulations show that this partitioning procedure does not affect the quality of clustering results significantly. Moreover, properly chosen weights can be assigned to each data element. This modification allows the presented algorithms to handle concept drift during simulations.

引用

页码：82 / 91

页数：10

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