Cluster a alysis 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 report, we present a novel ensemble minority clustering algorithm, Ewocs, suitable for weak clustering combination, and provide a theoretical proof of its properties under a loose set of constraints. The validity of the assumptions used in the proof is empirically assessed using a collection of synthetic datasets.
CitationGonzález, E.; Turmo, J. "Unsupervised ensemble minority clustering". 2012.
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