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Exploiting the analogy between the immune system and sparse distributed memory.

Hart, Emma and Ross, Peter (2003) Exploiting the analogy between the immune system and sparse distributed memory. Genetic Programming and Evolvable Machines, 4 (4). pp. 333-358. ISSN 1573 7632

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Abstract/Description

The relationship between immunological memory and a class of associative memories known as sparse distributed memories (SDM) is well known. This paper proposes a new model for clustering non-stationary data based on a combination of salient features from the two metaphors. The resulting system embodies the important principles of both types of memory; it is self-organising, robust, scalable, dynamic and can perform anomaly detection, and is shown to be a more faithful model of the biological system than a standard SDM. The model is first applied to clustering static benchmark data-sets, and is shown to outperform another system based on immunological principles. It is then applied to clustering non-stationary data-sets with promising results. The system is also shown to be scalable therefore is of potential for clustering real-world data-sets.

Item Type: Article
Print ISSN: 1573 7632
Electronic ISSN: 1389 2576
Uncontrolled Keywords: Genetic algorithm; Combination; Immunological memory; Sparse distributed memories; Application; Cluster detection; Non-stationery data; Performance evaluation;
University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Computing
Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods
000 Computer science, information & general works > 000 Computer science, knowledge & systems > 005 Computer programming, programs & data
Library of Congress Subjects: Q Science > QA Mathematics > QA76 Computer software
Item ID: 1754
Depositing User: RAE Import
Date Deposited: 18 Jul 2008 14:04
Last Modified: 10 Feb 2016 11:53
URI: http://researchrepository.napier.ac.uk/id/eprint/1754

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