Combining Multi-Layer Perceptron and K-means for data clustering with background knowledge

被引：0
|
作者：
Guan, Donghai [1 ]
Yuan, Weiwei [1 ]
Lee, Young-Koo [1 ]
Gavrilov, Andrey [1 ]
Lee, Sungyoung [1 ]
机构：
[1] Kyung Hee Univ, Dept Comp Engn, Seoul, South Korea
来源：
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF CONTEMPORARY INTELLIGENT COMPUTING TECHNIQUES | 2007年 / 2卷
关键词：
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：
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. To make use of this information, in this paper, we develop a new clustering method "MLP-KMEANS" by combining Multi-Layer Perceptron and K-means. We test our method on several data sets with partial constrains available. Experimental results show that our method can effectively improve clustering accuracy by utilizing available information.
引用
收藏
页码：1220 / +
页数：3
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