This paper studies the ensemble selection problem for un- supervised learning. Given a large library of different clustering solutions, our goal is to select a subset of solutions to form a smaller but better performing cluster ensemble than using all available solutions. We design our ensemble selection methods based on quality and diversity, the two factors that have been shown to influence cluster ensemble performance. Our investigation revealed that using quality or diversity alone may not consistently achieve improved performance. Based on our observations, we designed three different selection approaches that jointly consider these two factors. We empirically evaluated their performances in comparison with both full ensembles and a random selection strategy. Our results indicated that by explicitly considering both quality and diversity in ensemble selection, we can achieve statistically significant performance improvement over full ensembles.
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