Research Output

Clustering moving data with a modified immune algorithm.

  In this paper we present a prototype of a new model for performing clustering in large, non-static databases. Although many machine learning algorithms for data clustering have been proposed, none appear to specifically address the task of clustering moving data. The model we describe combines features of two existing computational models — that of Artificial Immune Systems (AIS) and Sparse Distributed Memories (SDM). The model is evolved using a coevolutionary genetic algorithm that runs continuously in order to dynamically track clusters in the data. Although the system is very much in its infancy, the experiments conducted so far show that the system is capable of tracking moving clusters in artificial data sets, and also incorporates some memory of past clusters. The results suggest many possible directions for future research

  • Type:

    Book Chapter

  • Date:

    30 November 2000

  • Publication Status:

    Published

  • Publisher

    Springer Berlin

  • DOI:

    10.1007/3-540-45365-2_41

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

Citation

Hart, E. & Ross, P. (2000). Clustering moving data with a modified immune algorithm. In Applications of Evolutionary Computing, 394-403. Springer Berlin. doi:10.1007/3-540-45365-2_41. ISBN 3-540-41920-9

Authors

Keywords

non-static databases; clustering; immune algorithm; moving data; artificial immune systems; sparse distributed memories; coevolutionary genetic algorithm;

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