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Clustering Moving Data with a Modified Immune Algorithm

  • Emma Hart
  • Peter Ross
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)

Abstract

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.

Keywords

Immune Algorithm Average Drop Cooperative Coevolution Hard Location Schema List 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Emma Hart
  • Peter Ross
    • 1
  1. 1.School of ComputingNapier UniversityEdinburghScotland

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