Clustering moving data with a modified immune algorithm.

Hart, Emma and Ross, Peter (2001) Clustering moving data with a modified immune algorithm. In: Applications of Evolutionary Computing. Lecture Notes in Computer Science, 2037/2 . Springer Berlin, pp. 394-403. ISBN 3-540-41920-9

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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

Item Type: Book Section
ISBN: 3-540-41920-9
Uncontrolled Keywords: non-static databases; clustering; immune algorithm; moving data; artificial immune systems; sparse distributed memories; coevolutionary genetic algorithm;
University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Computing
Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods > 006.3 Artificial intelligence
Library of Congress Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Item ID: 3223
Depositing User: Computing Research
Date Deposited: 23 Jul 2010 12:55
Last Modified: 14 Mar 2014 11:27

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