The growing hierarchical self-organizing map: Exploratory analysis of high-dimensional data

被引：313

作者：

Rauber, A ^{[1
]}

Merkl, D ^{[1
]}

Dittenbach, M ^{[1
]}

机构：

[1] Vienna Univ Technol, Dept Software Technol & Interact Syst, A-1040 Vienna, Austria

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2002年 / 13卷 / 06期

关键词：

data mining; exploratory data analysis; hierarchical clustering; pattern recognition; self-organizing map (SOM);

D O I：

10.1109/TNN.2002.804221

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The self-organizing map (SOM) is a very popular unsupervised neural-network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are related to the static architecture of this model as well as to the limited capabilities for the representation of hierarchical relations of the data. With our novel growing hierarchical SOM (GHSOM) presented in this paper, we address both limitations. The GHSOM is an artificial neural-network model with hierarchical architecture composed of independent growing SOMs. The motivation was to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data. Furthermore, by providing a global orientation of the independently growing maps in the individual layers of the hierarchy, navigation across branches is facilitated. The benefits of this novel neural network are a problem-dependent architecture and the intuitive representation of hierarchical relations in the data. This is especially appealing in explorative data mining applications, allowing the inherent structure of the data to unfold in a highly intuitive fashion.

引用

页码：1331 / 1341

页数：11

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