Exploiting the Analogy between the Immune System and Sparse Distributed Memories |
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Authors: | Emma Hart Peter Ross |
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Affiliation: | (1) Napier University, Scotland, UK |
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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. |
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Keywords: | immune system sparse distributed memory data-clustering |
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