A multi-act sequential game-based multi-objective clustering approach for categorical data
Refereed Original Article
Clustering categorical data, where no natural ordering can be found among the attributes values, has started drawing interest recently. Few clustering methods have been proposed to satisfy the categorical data requirements. Most of these methods have focused on optimizing a single measure, however, sev- eral applications in different areas need to consider multiple incommensurable criteria, often conﬂicting, during clustering. Motivated by this, we developed a multi-objective clustering approach for categori- cal data based on sequential games. It can automatically generate the correct number of clusters. The approach consists of three main phases. The ﬁrst phase identiﬁes initial clusters according to an initial- ization mechanism which has an important effect in the ﬁnal clustering result. The second phase uses multi-act multi-objective sequential two-player games in order to determine the appropriate number of clusters. A methodology based on backward induction is used to calculate a pure Nash equilibrium for each game. Finally, the third phase constructs homogenous clusters by optimizing intra-cluster inertia. The performance of this algorithm has been studied on both simulated and real-world datasets. Compar- isons with other clustering algorithms illustrate the effectiveness of the proposed approach.
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Date Accepted for Publication:
Sunday, 5 November, 2017
National University of Ireland, Dublin (UCD)
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