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•A novel clustering approach is proposed.•The approach encodes the objects to be clustered with binary signatures.•The binary signatures are extracted through a set of one-class models.•The approach is agnostic to the shape of clustering.•The proposed method favourably compares with state of the art.
In this paper, we present a novel clustering scheme based on binary embeddings, which provides compact and informative binary representations of high-dimensional objects. The binary representations are obtained with a collection of one-class classifiers learned from (pseudo) randomly selected points in the dataset. To cluster the binary representations, we consider two approaches: a mixture of Bernoulli distributions and a recent biclustering approach called CRAFT. The empirical evaluation in comparison with both classic and recent clustering methods, based on 12 different datasets, provides encouraging results. The main feature of the proposed method is that it is agnostic to the shape of the clusters.