This paper studies the ensemble selection problem for unsupervised learning. Given a large library of different clustering solutions, our goal is to select a subset of solutions to form a smaller yet 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 performance in comparison with both full ensembles and a random selection strategy. Our results indicate that by explicitly considering both quality and diversity in ensemble selection, we can achieve statistically significant performance improvement over full ensembles. Clustering for unsupervised data exploration and analysis has been investigated for decades in the statistics, data mining and machine learning communities. The goal of clustering is to group similar objects together based on some notion of similarity. Over the years, many clustering algorithms have been developed, each utilizing different distance/similarity measures and/or objective functions. Applying different methods, or the same methods with different parameter choices to the same data, we can obtain varying clustering results. A fundamental question is: given so many possible options, how should we choose among them? One possible answer to this question is that we do not need to choose at all; because we can leverage these different options by applying all of them and then combining their clustering results. This is the basic philosophy behind cluster ensembles [20] , which have gained increasing popularity in the clustering community [5, 6, 10, 11, 13, 21, 22, 23] in recent years. A cluster ensemble framework typically produces a large set of clustering results and then combines them
Fern and Lin REF proposed a clustering ensemble selection framework which selects a subset of partitions from a large library of partitions.
Cluster ensemble selection
In SDM
Computer Science