1. A computer implemented method for processing a data structure representing a dependency matrix having a plurality of columns representing respective first components and a plurality of rows representing respective second components, the method comprising: 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; assigning each component of the first and second components an affiliation vector f u and f i respectively indicative of the strength of affiliation of the component to N predefined initial clusters of cells of the matrix, and initializing the affiliation vectors with predefined values; determining a probability model using the affiliations vectors f u and f i as parameters, wherein the probability model presents the probabilities for a first component of the first components to depend on a second component of the second components; estimating the parameters of the probability model for a plurality of different numbers of clusters starting from the initial number N of clusters using the matrix; computing a score for the parameters of the probability model estimated for each of the plurality of different numbers of clusters; selecting the parameters of the probability model with the highest computed score; using the selected parameters of the probability model to identify cells of unknown dependencies pairs of first and second components that depend on each other.