Weighted Topological Clustering for Categorical Data

被引：0
|
作者：
Rogovschi, Nicoleta [1 ]
Nadif, Mohamed [1 ]
机构：
[1] Paris Descartes Univ, LIPADE, 45 Rue St Peres, F-75006 Paris, France
来源：
NEURAL INFORMATION PROCESSING, PT I | 2011年 / 7062卷
关键词：
unsupervised learning; mixture models; Self-Organizing Maps; categorical data; SELF-ORGANIZING MAPS; ALGORITHM; MODEL; VISUALIZATION;
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：
This paper introduces a probabilistic self-organizing map for topographic clustering, analysis of categorical data. By considering a parsimonious mixture model, we present a new probabilistic Self-Organizing Map (SOM). The estimation of parameters is performed by the EM algorithm. Contrary to SOM. our proposed learning algorithm optimizes an objective function. Its performance is evaluated on real datasets.
引用
收藏
页码：599 / +
页数：3
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