document: Deriving quantitative models for correlation clusters Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a common arbitrarily oriented hyperplane of arbitrary dimensionality . Several algorithms for this task have been proposed recently . However , all algorithms only compute the partitioning of the data into clusters . This is only a first step in the pipeline of advanced data analysis and system modelling . The second ( post-clustering ) step of deriving a quantitative model for each correlation cluster has not been addressed so far . In this paper , we describe an original approach to handle this second step . We introduce a general method that can extract quantitative information on the linear dependencies within a correlation clustering . Our concepts are independent of the clustering model and can thus be applied as a post-processing step to any correlation clustering algorithm . Furthermore , we show how these quantitative models can be used to predict the probability distribution that an object is created by these models . Our broad experimental evaluation demonstrates the beneficial impact of our method on several applications of significant practical importance .
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