Factor Controlled Hierarchical SOM Visualization for Large Set of Data

被引：0

作者：

Chakma, Junan ^{[1
]}

Umemura, Kyoji ^{[1
]}

机构：

[1] Dept. of Information Science, Toyohashi University of Technology, Toyohashi-shi, 441-8580, Japan

来源：

IEICE Transactions on Information and Systems | 2003年 / E86-D卷 / 09期

关键词：

Algorithms - Automata theory - Data reduction - Error analysis - Vectors - Visualization;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Self-organizing map is a widely used tool in high-dimensional data visualization. However, despite its benefits of plotting very high-dimensional data on a low-dimensional grid, browsing and understanding the meaning of a trained map turn to be a difficult task - specially when number of nodes or the size of data increases. Though there are some well-known techniques to visualize SOMs, they mainly deals with cluster boundaries and they fail to consider raw information available in original data in browsing SOMs. In this paper, we propose our Factor controlled Hierarchical SOM that enables us select number of data to train and label a particular map based on a pre-defined factor and provides consistent hierarchical SOM browsing.

引用

页码：1796 / 1803

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