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Recent high-throughput technologies enable the acquisition of a variety of complementary data and imply regulatory networks on the systems biology level. A common approach to the reconstruction of such networks is the cluster analysis which is based on a similarity measure. We use the information theoretic concept of the mutual information, that has been originally defined for discrete data, as a measure of similarity and propose an extension to a commonly applied algorithm for its calculation from continuous biological data. We compare our approach to previously existing algorithms. We develop a performance optimised software package for the application of the mutual information to large-scale datasets. Furthermore, we design and implement a web-based service for the analysis of integrated data measured with different technologies. Application to biological data reveals biologically relevant groupings and reconstructed signalling networks show agreements with physiological findings.