Clumping with feature selection and Occam´s razor

A clustering technique for binary vectors is described that permits the overlapping of clusters (clumping) and selects for each cluster a subset of relevant features. This technique does not require the user to set any parameters (e.g. number of clusters, degree of overlap). The clustering problem is rigorously defined as that of the minimization of a cost function. This preference criterion called the `minimal representation criterion´, represents a tradeoff between the fit of data to clusters and the simplicity of cluster configuration and may be considered a quantitative Occam´s razor. The central component of the presented method is an iterative algorithm that converges to a local minimum of the cost function