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CRAFT: ClusteR-specific Assorted Feature selecTion
[article]
2015
arXiv
pre-print
We present a framework for clustering with cluster-specific feature selection. The framework, CRAFT, is derived from asymptotic log posterior formulations of nonparametric MAP-based clustering models. CRAFT handles assorted data, i.e., both numeric and categorical data, and the underlying objective functions are intuitively appealing. The resulting algorithm is simple to implement and scales nicely, requires minimal parameter tuning, obviates the need to specify the number of clusters a priori,
arXiv:1506.07609v1
fatcat:mkbjg3dcqvh5happctzzukik7q