Sales intelligence using web mining.

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dc.contributor.author Popova, V.
dc.contributor.author John, Robert, 1955-
dc.contributor.author Stockton, David
dc.date.accessioned 2010-02-18T15:11:24Z
dc.date.available 2010-02-18T15:11:24Z
dc.date.issued 2009
dc.identifier.citation Popova, V., John, R. and Stockton, D. (2009) Sales intelligence using web mining. In: P. Perner (ed): Advances in Data Mining: Proceedings of 9th Industrial Conference on Data Mining (ICDM´09), Lecture Notes in Artificial Intelligence, Springer, pp.131-145. en
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/2086/3456
dc.description.abstract This paper presents a knowledge extraction system for providing sales intelligence based on information downloaded from the WWW. The information is first located and downloaded from relevant companies’ websites and then machine learning is used to find these web pages that contain useful information where useful is defined as containing news about orders for specific products. Several machine learning algorithms were tested from which k-nearest neighbour, support vector machines, multi-layer perceptron and C4.5 decision tree produced best results in one or both experiments however k-nearest neighbour and support vector machines proved to be most robust which is a highly desired characteristic in the particular application. K-nearest neighbour slightly outperformed the support vector machines in both experiments which contradicts the results reported previously in the literature. en
dc.language.iso en en
dc.publisher Springer Berlin en
dc.subject web mining en
dc.subject text mining en
dc.subject machine learning en
dc.subject natural language processing en
dc.title Sales intelligence using web mining. en
dc.type Book chapter en
dc.identifier.doi http://dx.doi.org/10.1007/978-3-642-03067-3_12
dc.researchgroup Manufacturing Research en


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