Multi-objective genetic algorithm for supervised clustering
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2018
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2561
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eng
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application/pdf
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121 leaves
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b207807
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National Institute of Development Administration. Library and Information Center
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Vipa Thananant (2018). Multi-objective genetic algorithm for supervised clustering. Retrieved from: https://repository.nida.ac.th/handle/662723737/6424.
Title
Multi-objective genetic algorithm for supervised clustering
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Abstract
Supervised clustering organizes data instances into clusters on the basis of
similarities between the data instances as well as class labels for the data instances.
Supervised clustering seeks to meet multiple objectives, such as compactness of
clusters, homogeneity of data in clusters with respect to their class labels, and
separateness of clusters. With these objectives in mind, a new supervised clustering
algorithm based on a multi-objective crowding genetic algorithm, named SC-MOGA,
is proposed in this thesis. The algorithm searches for the optimal clustering solution
that simultaneously achieves the three objectives mentioned above. The SC-MOGA
performs very well on a small dataset, but for a large dataset it may not be able to
converge to an optimal solution or can take a very long running time to converge to a
solution. Hence, a data sampling method based on the Bisecting K-Means algorithm is
also introduced, to find representatives for supervised clustering. This method groups
the data instances of a dataset into small clusters, each containing data instances with
the same class label. Data representatives are then randomly selected from each
cluster. The experimental results show that SC-MOGA with the proposed data
sampling method is very effective. It outperforms three previously proposed
supervised clustering algorithms, namely SRIDHCR, LK-Means and SCEC, in terms
of four cluster validity indexes. The experimental results show that the proposed data
sampling method not only helps to reduce the number of data instances to be clustered
by the SC-MOGA, but also enhances the quality of the data clustering results. Moreover, the biased initial approach is proposed in this thesis to find a good initial
population to bias. The experimental results show that biased initial population of SCMOGA will improve clustering quality and the more percentage of biased initial
population the better clustering quality.
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Thesis (Ph.D. (Computer Science and Information Systems))--National Institute of Development Administration, 2018