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Web Mining for Business Intelligence: Discovering Novel Association Rules from Competitors’ Websites

Web Mining for Business Intelligence: Discovering Novel Association Rules from Competitors’ Websites
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Author(s): Xin Chen (New Jersey Institute of Technology, USA) and Yi-fang Brook Wu (New Jersey Institute of Technology, USA)
Copyright: 2005
Pages: 4
Source title: Managing Modern Organizations Through Information Technology
Source Editor(s): Mehdi Khosrow-Pour (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59140-822-2.ch166

Abstract

The Web has offered companies a convenient way to publish their information and conduct business with customers and partners, and it also opens an opportunity to companies to acquire knowledge of their competitors. Such knowledge is critical for a company to define its business strategies and to establish a network with business partners. This paper proposes a content web mining technique that discovers novel association rules among noun phrases extracted from web pages on one’s competitors’ websites. Novelty of an association rule is measured as the distance between the antecedent and the consequent of the rule in the background knowledge, which is developed from documents on one’s own website are. Incorporating background knowledge into the web mining process enables us to discover previously unknown but potential useful patterns. A running example demonstrates that the novelty prediction accuracy is high in terms of correlating with human judgments.

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