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.
Please help me write a proper abstract based on the patent claims. CLAIM: 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 fu and fi 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 fu and fi 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. 2-14. (canceled) 15. A computer system 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 computer system being configure for: 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 fu and fi 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 fu and fi 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. 16. A computer program product comprising a computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code configured to implement a 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 fu and fi 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 fu and fi 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. 17. The computer program product of claim 16, wherein the probability model is a function of the inner product <fu, fi> between two affiliation vectors fu and fi in corresponding to the first and second components, where c runs over each cluster of the N clusters. 18. The computer program product of claim 16, wherein the probability model is given by the following equation: 1−. 19. The computer program product of claim 16, wherein the data structure is representable as an adjacency matrix of a bipartite graph, preferably a bipartite graph of components versus component attributes. 20. The computer program product of claim 19, wherein the probabilities for a first component of the first components to depend on a second component of the second components comprises the probability that at least one of: the first component accesses data in the second component; the first component requests a service of the second component.