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Towards a danger theory inspired artificial immune system for web mining

Secker, Andrew D. and Freitas, Alex A. and Timmis, Jon (2005) Towards a danger theory inspired artificial immune system for web mining. In: Scime, A., ed. Web Mining: applications and techniques. Idea Group, pp. 145-168. ISBN 1-59140-415-0. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:14372)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)

Abstract

The natural immune system exhibits many properties that are of interest to the area of web mining. Of particular

information from the web. As part of a larger project to construct a large-scale dynamic web-mining system, this

Classification (AISEC) is described in detail and compared with a traditional approach of naive Bayesian

the Danger Theory from immunology can be used to further improve the performance of such an artificial

immune system.

Item Type: Book section
Uncontrolled keywords: artificial immune systems, web mining, data mining, e-mail classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:03 UTC
Last Modified: 16 Feb 2021 12:25 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14372 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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