期刊
NEURAL COMPUTING & APPLICATIONS
卷 15, 期 1, 页码 9-17出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-005-0002-1
关键词
Kohonen self-organising map; rule extraction; data mining; knowledge discovery
The Kohonen self-organising feature map (SOM) has several important properties that can be used within the data mining/knowledge discovery and exploratory data analysis process. A key characteristic of the SOM is its topology preserving ability to map a multi-dimensional input into a two-dimensional form. This feature is used for classification and clustering of data. However, a great deal of effort is still required to interpret the cluster boundaries. In this paper we present a technique which can be used to extract propositional IF..THEN type rules from the SOM network's internal parameters. Such extracted rules can provide a human understandable description of the discovered Clusters.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据