Abstract
Techniques from the domain of Artificial Intelligence are used increasingly to combat the problem of information overload on the Internet. The vast majority of such techniques and related systems attempt to overcome the problems of information overload by automating the analysis of the content of online documents. In many web-sites (document repositories and e-commerce sites) system logs, documenting user behaviour, are available and can be used as a valuable resource in information management. This information represents a valuable resource to aid in the organisation of information and the presentation of such information to users. In many such systems this information can be represented using a set of tuples indicating which pages/items were visited by a user. Using this information can provide many advantages. A classification of tuples can be used to aid information management and we outline briefly systems which have used the classification algorithm we propose. This paper presents an approach to solving classification problems by combining feature selection and neural networks. The main idea is to use techniques from the field of information theory to select a set of important attributes that can be used to classify tuples. A neural network is trained using these attributes; the neural network is then used to classify tuples. In this paper, we discuss data mining, review common approaches and outline our algorithm. We also present preliminary results obtained against a well-known data collection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aggrawal, C.C., Yu, P.S. (1999): Data Mining Techniques for Associations, Clustering and Classification. Proceedings of the 3rd Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining (PAKDD-99).
Dumais, S. (1991): Improving the Retrieval of Information from External Sources. Behavior Research Methods Instruments and Computers. Vol. 2, No. 23, pp. 229 — 236.
Mehta, M., Shafer, J., Agrawal, R. (1996): SPRINT: A Scalable Parallel Classifier for Data Mining. Proceedings of the 22nd VLDB Conference.
Kleinberg, J. (1997): Authoritative Sources in a Hyperlinked Environment. IBM Research Report RJ 10076, May, 1997.
Lu, FL, Setioni, R., Liu, H. (1995): NeuroRule: A Connectionist Approach to Data Mining. Proceedings of the 21st VLDB Conference.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Reidl, J. (1994): GroupLens: An Open Architecture for Collaborative Filtering of NetNews. Proceedings of ACM 1994 Conference on CSCW. pp. 175 — 186.
Salton, G.A., McGill, M.J. (1983): Introduction to Modern Information Retrieval. McGraw Hill International, 1983.
Shannon, C. (1948): A Mathematical Theory of Communication. Technical Report, Bell Systems.
Shardanand, U., Maes, P. (1995): Social Information Filtering: Algorithms for Automating “Word of Mouth”. Computer-Human Interfaces (CHI ‘85).
Terveen, L., Hill, W., Pimento, B., McDonald, D. (1997): PHOAKS: A system for sharing Recommendations. Communications of the ACM. Vol. 40, No. 3, pp. 59–65.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Griffith, J., O’Dea, P., O’Riordan, C. (2003). A Neural Net Approach to Data Mining: Classification of Users to Aid Information Management. In: Szczepaniak, P.S., Segovia, J., Kacprzyk, J., Zadeh, L.A. (eds) Intelligent Exploration of the Web. Studies in Fuzziness and Soft Computing, vol 111. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1772-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1772-0_23
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2519-0
Online ISBN: 978-3-7908-1772-0
eBook Packages: Springer Book Archive