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Abstract

We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the clusters. The algorithms work by maximizing the dependence
between the taxonomy and the original data. The resulting taxonomy is a more informative visualization of complex data than simple clustering; in addition, taking into account the relations between different clusters is shown to
substantially improve the quality of the clustering, when compared with state-ofthe-art algorithms in the literature (both spectral clustering and a previous dependence
maximization approach). We demonstrate our algorithm on image and text data.