document: Robust information-theoretic clustering How do we find a natural clustering of a real world point set , which contains an unknown number of clusters with different shapes , and which may be contaminated by noise ? Most clustering algorithms were designed with certain assumptions ( Gaussianity ) , they often require the user to give input parameters , and they are sensitive to noise . In this paper , we propose a robust framework for determining a natural clustering of a given data set , based on the minimum description length ( MDL ) principle . The proposed framework , Robust Information-theoretic Clustering ( RIC ) , is orthogonal to any known clustering algorithm : given a preliminary clustering , RIC purifies these clusters from noise , and adjusts the clusterings such that it simultaneously determines the most natural amount and shape ( subspace ) of the clusters . Our RIC method can be combined with any clustering technique ranging from K-means and K-medoids to advanced methods such as spectral clustering . In fact , RIC is even able to purify and improve an initial coarse clustering , even if we start with very simple methods such as grid-based space partitioning . Moreover , RIC scales well with the data set size . Extensive experiments on synthetic and real world data sets validate the proposed RIC framework .
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