Research Output

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

  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.

  • Type:

    Article

  • Date:

    31 December 2003

  • Publication Status:

    Published

  • Publisher

    Kluwer Academic

  • DOI:

    10.1023/a:1026191011609

  • Cross Ref:

    5144847

  • ISSN:

    1389-2576

  • Library of Congress:

    QA76 Computer software

Citation

Hart, E., & Ross, P. (2002). Exploiting the analogy between the immune system and sparse distributed memory. Genetic Programming and Evolvable Machines, 4(4), 333-358. doi:10.1023/a:1026191011609

Authors

Keywords

Genetic algorithm; Combination; Immunological memory; Sparse distributed memories; Application; Cluster detection; Non-stationery data; Performance evaluation;

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