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  1. Proceedings of the 2nd international workshop on Web information and data management (WIDM '99)
  2. A Web text mining approach based on self-organizing map
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An efficient plan execution system for information management agents
Rapper: a wrapper generator with linguistic knowledge
An adaptive real-time Web search engine
Automatic migration of files into relational databases
Controlled access and dissemination of XML documents
Optimising Web queries using document type definitions
Selectively materializing data in mediators by analyzing source structure, query distribution and maintenance cost
Efficient spatial data transmission in Web-based GIS
Data mining and the Web: past, present and future
Mining the Web for acronyms using the duality of patterns and relations
INsite: introduction to a generic paradigm for interpreting user-Web space interaction
A Web text mining approach based on self-organizing map
Warehousing and mining Web logs

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A Web text mining approach based on self-organizing map

Content Provider ACM Digital Library
Author Yang, Hsin-Chang Lee, Chung-Hong
Abstract Web text mining is a new issue in the knowledge discovery research field. It is aimed to help people discover knowledge from large quantities of semi-structured or unstructured text in the web. Several approaches, including some pure and hybrid information retrieval (IR) methods, have been proposed to tackle such an issue. Among these approaches, combining the Self-Organizing Map (SOM) method with the principles of the vectorspace model, appears to be a promising alternative for the traditional purely IR-based methods in this problem domain. In this paper, a novel SOM-based method using a Chinese corpus for web text mining is presented. The SOM is used to generate two maps, namely the word cluster map and the document cluster map, which reveal the relationships among words and documents respectively. The search process incorporates these two maps and effectively finds the relevant documents according to the keywords specified in the query. The conceptually associated web documents are found not only by the specific keywords but the relevant words found by the word cluster map.
Starting Page 59
Ending Page 62
Page Count 4
File Format PDF
ISBN 1581132212
DOI 10.1145/319759.319789
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 1999-11-01
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Document clustering Self-organizing map Text data mining
Content Type Text
Resource Type Article
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