Abstract
In this paper, we propose a new encoding scheme for GA and employ multiple objectives in handling the clustering problem. The proposed encoding scheme uses links so that objects to be clustered form a linear pseudo-graph. As multiple objectives are concerned, we used two objectives: 1) to minimize the Total Within Cluster Variation (TWCV); and 2) minimizing the number of 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 GA run. The performance of the proposed approach has been tested using two well-known data sets: Iris and Ruspini. The obtained results demonstrate improvement over classical approaches.
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Du, J., Korkmaz, E., Alhajj, R., Barker, K. (2004). Novel Clustering Approach that Employs Genetic Algorithm with New Representation Scheme and Multiple Objectives. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2004. Lecture Notes in Computer Science, vol 3181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30076-2_22
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DOI: https://doi.org/10.1007/978-3-540-30076-2_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22937-7
Online ISBN: 978-3-540-30076-2
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