Websom for Textual Data Mining

被引:2
作者
Krista Lagus
Timo Honkela
Samuel Kaski
Teuvo Kohonen
机构
[1] Helsinki University of Technology,Neural Networks Research Centre
来源
Artificial Intelligence Review | 1999年 / 13卷
关键词
data mining; document filtering; exploratory data analysis; information retrieval; self-organizing map; SOM; text document collection; WEBSOM;
D O I
暂无
中图分类号
学科分类号
摘要
New methods that are user-friendly and efficient are needed for guidanceamong the masses of textual information available in the Internet and theWorld Wide Web. We have developed a method and a tool called the WEBSOMwhich utilizes the self-organizing map algorithm (SOM) for organizing largecollections of text documents onto visual document maps. The approach toprocessing text is statistically oriented, computationally feasible, andscalable – over a million text documents have been ordered on a single map.In the article we consider different kinds of information needs and tasksregarding organizing, visualizing, searching, categorizing and filteringtextual data. Furthermore, we discuss and illustrate with examples howdocument maps can aid in these situations. An example is presented wherea document map is utilized as a tool for visualizing and filtering a stream ofincoming electronic mail messages.
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页码:345 / 364
页数:19
相关论文
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