Abstract
In this paper, we present a linked-list based encoding scheme for multiple objectives based genetic algorithm (GA) to identify clusters in a partition. Our approach obtains the optimal partitions for all the possible numbers of clusters in the Pareto Optimal set returned by a single genetic GA run. The performance of the proposed approach has been tested using two well-known data sets, namely Iris and Ruspini. The obtained results are promising and demonstrate the applicability and effectiveness of the proposed approach.
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Du, J., Korkmaz, E.E., Alhajj, R., Barker, K. (2005). Alternative Clustering by Utilizing Multi-objective Genetic Algorithm with Linked-List Based Chromosome Encoding. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_34
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DOI: https://doi.org/10.1007/11510888_34
Publisher Name: Springer, Berlin, Heidelberg
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