We build a general and easily applicable clustering theory, which we call crossentropy clustering (shortly CEC), which joins the advantages of classical kmeans (easy implementation and speed) with those of EM (ane invariance and ability to adapt to clusters of desired shapes). Moreover, contrary to k-means and EM, CEC nds the optimal number of clusters by automatically removing groups which have negative information cost.