A Multi-Agent Neural Network System for Web Text Mining

View Sample PDF

Author(s): Lean Yu (Chinese Academy of Sciences, China), Shouyang Wang (Chinese Academy of Sciences, China)and Kin Keung Lai (City University of Hong Kong, China)
Copyright: 2009
Pages: 26
Source title: Handbook of Research on Text and Web Mining Technologies
Source Author(s)/Editor(s): Min Song (New Jersey Institute of Technology, USA)and Yi-Fang Brook Wu (New Jersey Institute of Technology, USA)
DOI: 10.4018/978-1-59904-990-8.ch013

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

Purchase

View A Multi-Agent Neural Network System for Web Text Mining on the publisher's website for pricing and purchasing information.

Abstract

With the rapid increase of the huge amount of online information, there is a strong demand for Web text mining which helps people discover some useful knowledge from Web documents. For this purpose, this chapter first proposes a back-propagation neural network (BPNN)-based Web text mining system for decision support. In the BPNN-based Web text mining system, four main processes, Web document search, Web text processing, text feature conversion, and BPNN-based knowledge discovery, are involved. Particularly, BPNN is used as an intelligent learning agent that learns about underlying Web documents. In order to scale the individual intelligent agent with the large number of Web documents, we then provide a multi-agent-based neural network system for Web text mining in a parallel way. For illustration purpose, a simulated experiment is performed. Experiment results reveal that the proposed multi-agent neural network system is an effective solution to large scale Web text mining.

The IRMA Community

Research IRM

A Multi-Agent Neural Network System for Web Text Mining

Purchase

Abstract

Related Content

IRMA Sponsors