@inbook{18aced56867749fca0b86208f5c6f719,
title = "A danger theory inspired approach to web mining",
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.",
keywords = "danger signal, artificial immune system, alarm signal, danger area, artificial immune recognition system",
author = "Andrew Secker and Freitas, {Alex A.} and Jon Timmis",
year = "2003",
doi = "10.1007/978-3-540-45192-1_16",
language = "English",
isbn = "3540407669",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "156--167",
editor = "Jon Timmis and Peter Bentley and Emma Hart",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Switzerland",
}