Probabilistic self-organizing maps for qualitative data

被引：14

作者：

Lopez-Rubio, Ezequiel ^{[1
]}

机构：

[1] Univ Malaga, Dept Comp Languages & Comp Sci, E-29071 Malaga, Spain

来源：

NEURAL NETWORKS | 2010年 / 23卷 / 10期

关键词：

Self organizing maps; Categorical data; Qualitative data; Discrete probability distribution; Unsupervised learning; Stochastic approximation; NETWORK INTRUSION DETECTION; MULTIVARIATE-BERNOULLI; CATEGORICAL-DATA; NEURAL-NETWORKS; MISSING VALUES; LATENT CLASS; MODELS; DISTRIBUTIONS; IMPUTATION; MIXTURE;

D O I：

10.1016/j.neunet.2010.07.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a self-organizing map model to study qualitative data (also called categorical data) It is based on a probabilistic framework which does not assume any prespecified distribution of the input data Stochastic approximation theory is used to develop a learning rule that builds an approximation of a discrete distribution on each unit This way the internal structure of the input dataset and the correlations between components are revealed without the need of a distance measure among the input values Experimental results show the capabilities of the model in visualization and unsupervised learning tasks (C) 2010 Elsevier Ltd All rights reserved

引用

页码：1208 / 1225

页数：18

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