A danger theory inspired approach to web mining

Andrew Secker*, Alex A. Freitas, Jon Timmis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

35 Citations (SciVal)

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsJon Timmis, Peter Bentley, Emma Hart
PublisherSpringer Nature
Pages156-167
Number of pages12
ISBN (Print)3540407669, 9783540407669
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2787
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • danger signal
  • artificial immune system
  • alarm signal
  • danger area
  • artificial immune recognition system

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