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Cluster analysis lies at the core of most unsupervised learning tasks. However, the majority of clustering algorithms depend on the all-in assumption, in which all objects belong to some cluster, and perform poorly on minority clustering tasks, in which a small fraction of signal data stands against a majority of noise. The approaches proposed so far for minority clustering are supervised: they require the number and distribution of the foreground and background clusters. In supervised learning and all-in clustering, combination methods have been successfully applied to obtain distribution-free learners, even from the output of weak individual algorithms.
In this work, we propose a novel ensemble minority clustering algorithm, EWOCS, suitable for weak clustering combination. Its properties have been theoretically proved under a loose set of constraints. We also propose a number of weak clustering algorithms, and an unsupervised procedure to determine the scaling parameters for Gaussian kernels used within the task.
We have implemented a number of approaches built from the proposed components, and evaluated them on a collection of datasets.
CitationGonzàlez, E., Turmo, J. Unsupervised ensemble minority clustering. "Machine learning", Gener 2015, vol. 98, núm. 1-2, p. 217-268.
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