Sales Intelligence Using Web Mining

  • Authors:
  • Viara Popova;Robert John;David Stockton

  • Affiliations:
  • Centre for Manufacturing, De Montfort University, Leicester, UK LE1 9BH;Centre for Computational Intelligence, De Montfort University, Leicester, UK LE1 9BH;Centre for Manufacturing, De Montfort University, Leicester, UK LE1 9BH

  • Venue:
  • ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
  • Year:
  • 2009

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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.