.png){kind=icon}
.png){kind=icon}
Return
Websom for textual data mining
.png){kind=icon}
PRE
AI
DOI：10.1023/A:1006586221250.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
New methods that are user-friendly and efficient are needed for guidance among the masses of textual information available in the Internet and the World Wide Web. We have developed a method and a tool called the WEBSOM which utilizes the self-organizing map algorithm (SOM) for organizing large collections of text documents onto visual document maps. The approach to processing text is statistically oriented, computationally feasible, and scalable - over a million text documents have been ordered on a single map. In the article we consider different kinds of information needs and tasks regarding organizing, visualizing, searching, categorizing and filtering textual data. Furthermore, we discuss and illustrate with examples how document maps can aid in these situations. An example is presented where a document map is utilized as a tool for visualizing and filtering a stream of incoming electronic mail messages.
Keywords:
data mining
document filtering
exploratory data analysis
information retrieval
self-organizing map
SOM
text document collection
WEBSOM
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.9
Papers:
6.1K
Citations:
1.9W
Organization
No organization information available