A Danger Theory Approach to Web Mining

Secker, Andrew D. and Freitas, Alex A. and Timmis, Jon (2003) A Danger Theory Approach to Web Mining. In: Proceedings of the 2nd International Conference on Artificial Immune Systems, SEP 01-03, 2003, Edinburgh Scotland. (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)

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Within immunology, new theories are constantly being proposed that challenge current ways of thinking. These include new theories regarding how the immune system responds to pathogenic material. This conceptual paper takes one relatively new such theory: the Danger theory, and explores the relevance of this theory to the application domain of web mining. Central to the idea of Danger theory is that of a context dependant response to invading pathogens. This paper argues that this context dependency could be utilised as powerful metaphor for applications in web mining. An illustrative example adaptive mailbox filter is presented that exploits properties of the immune system, including the Danger theory. This is essentially a dynamical classification task: a task that this paper argues is well suited to the field of artificial immune systems, particularly when drawing inspiration from the Danger theory.

Item Type: Conference or workshop item (UNSPECIFIED)
Uncontrolled keywords: artificial immune systems, danger theory, data mining, web mining
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:00
Last Modified: 17 Jun 2014 12:56
Resource URI: https://kar.kent.ac.uk/id/eprint/13908 (The current URI for this page, for reference purposes)
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