An extension of self-organizing maps to categorical data

被引：0
|
作者：
Chen, N [1 ]
Marques, NC
机构：
[1] Chinese Acad Sci, Inst Mech, Beijing 100080, Peoples R China
[2] Univ Nova Lisboa, Fac Ciencias & Tecnol, Dept Informat, CENTRIA, P-2829516 Caparica, Portugal
来源：
PROGRESS IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2005年 / 3808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：
Self-organizing maps (SOM) have been recognized as a powerful tool in data exploratoration, especially for the tasks of clustering on high dimensional data. However, clustering on categorical data is still a challenge for SOM. This paper aims to extend standard SOM to handle feature values of categorical type. A batch SOM algorithm (NCSOM) is presented concerning the dissimilarity measure and update method of map evolution for both numeric and categorical features simultaneously.
引用
收藏
页码：304 / 313
页数：10
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