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 15737632
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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.
|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|
|Depositing User:||RAE Import|
|Date Deposited:||18 Jul 2008 15:04|
|Last Modified:||12 Jan 2011 04:46|
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