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.
KeywordsImmune Algorithm Average Drop Cooperative Coevolution Hard Location Schema List
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