Component Clustering Based on Maximal Association**This work was funded by IBM Canada Ltd. Laboratory - Center for Advanced Studies (Toronto) and the National Research Council of Canada.

In this paper, we present a supervised clustering framework for recovering the architecture of a software system. The technique measures the association between the system components (such as files) in terms of data and control flow dependencies among the groups of highly related entities that are scattered throughout the components. The application of data mining techniques allows to extract the maximum association among the groups of entities. This association is used as a measure of closeness among the system files in order to collect them into subsystems using an optimization clustering technique. A two-phase supervised clustering process is applied to incrementally generate the clusters and control the quality of the system decomposition. In order to address the complexity issues, the whole clustering space is decomposed into sub-spaces based on the association property. At each iteration, the sub-spaces are analyzed to determine the most eligible sub-space for the next cluster, which is then followed by an optimization search to generate a new cluster.