Embodiments include processing a data structure representing a dependency matrix having columns representing respective first components and rows representing respective second components. Aspects include assigning each cell of the matrix a value indicative of the level of dependency or indicative of an unknown dependency of a pair of first and second components forming the cell and assigning each component of the first and second components an affiliation vector indicative of the strength of affiliation of the component to N predefined initial clusters of cells of the matrix. Aspects also include determining a probability model using the affiliations vectors parameters and estimating the parameters of the probability model for a plurality of different numbers of clusters starting from the initial number N of clusters. Aspects further include computing a score for the parameters of the probability model estimated and selecting the parameters of the probability model with the highest computed score.