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

Hart, Emma; Ross, Peter

Abstract

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.

Citation

Hart, E., & Ross, P. (2003). Exploiting the analogy between the immune system and sparse distributed memory. Genetic Programming and Evolvable Machines, 4(4), 333-358. https://doi.org/10.1023/a%3A1026191011609

• Journal Article Type: Article
• Publication Date: 2003
• Deposit Date: Jul 18, 2008
• Print ISSN: 1389-2576
• Electronic ISSN: 1573-7632
• Publisher: BMC
• Peer Reviewed: Peer Reviewed
• Volume: 4
• Issue: 4
• Pages: 333-358
• DOI: https://doi.org/10.1023/a%3A1026191011609
• Keywords: Genetic algorithm; Combination; Immunological memory; Sparse distributed memories; Application; Cluster detection; Non-stationery data; Performance evaluation
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/1754
• Publisher URL: http://dx.doi.org/10.1023/A:1026191011609