11. A system for unsupervised relational clustering of data, adapted to receive a data set comprising a plurality of interrelated data objects having data associated with latent classes, having at least one of respective data object attributes, homogeneous relations between the data objects having the same type, and heterogeneous relations between the data objects having different types, and to cluster the data objects, comprising: a memory configured to store the received data set and the at least one of respective data object attributes, homogeneous relations between the data objects having the same type, and heterogeneous relations between the data objects having different types, and to cluster the data objects; a memory configured to store parameters of an unsupervised mixed membership relational clustering model, based on the different latent classes, respective data object attributes, available homogeneous relations between the data objects having the same type, and available heterogeneous relations between the data objects having different types; and at least one automated processor configured to: generate latent indicators for the plurality of data objects, comprising respective latent class membership parameters based on a predetermined distribution, wherein the latent indicators have respective latent class membership parameters generated based on a distribution selected from the group consisting of: a multinomial distribution; a Bernoulli distribution; a normal distribution; and an exponential distribution; automatically optimize the mixed membership relational clustering model by updating the parameters to maximize a likelihood function to estimate unknown parameters of a joint probability distribution matrix over the latent indicators of the plurality of data objects with respect to: observations of the data object attributes, the homogeneous relations between the respective data object and data objects having the same type if available, and the heterogeneous relations between the respective data object and data objects having different types if available, use the optimized mixed membership relational clustering model to partition an arbitrarily complex graph involving at least the data object attributes, the homogeneous relations and the heterogeneous relations; produce an output representing the partitioned graph.
11. A system for unsupervised relational clustering of data, adapted to receive a data set comprising a plurality of interrelated data objects having data associated with latent classes, having at least one of respective data object attributes, homogeneous relations between the data objects having the same type, and heterogeneous relations between the data objects having different types, and to cluster the data objects, comprising: a memory configured to store the received data set and the at least one of respective data object attributes, homogeneous relations between the data objects having the same type, and heterogeneous relations between the data objects having different types, and to cluster the data objects; a memory configured to store parameters of an unsupervised mixed membership relational clustering model, based on the different latent classes, respective data object attributes, available homogeneous relations between the data objects having the same type, and available heterogeneous relations between the data objects having different types; and at least one automated processor configured to: generate latent indicators for the plurality of data objects, comprising respective latent class membership parameters based on a predetermined distribution, wherein the latent indicators have respective latent class membership parameters generated based on a distribution selected from the group consisting of: a multinomial distribution; a Bernoulli distribution; a normal distribution; and an exponential distribution; automatically optimize the mixed membership relational clustering model by updating the parameters to maximize a likelihood function to estimate unknown parameters of a joint probability distribution matrix over the latent indicators of the plurality of data objects with respect to: observations of the data object attributes, the homogeneous relations between the respective data object and data objects having the same type if available, and the heterogeneous relations between the respective data object and data objects having different types if available, use the optimized mixed membership relational clustering model to partition an arbitrarily complex graph involving at least the data object attributes, the homogeneous relations and the heterogeneous relations; produce an output representing the partitioned graph. 14. The system according to claim 11 , wherein: the data set is represented as {{F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m }; attributes of respective data objects are represented as {Θ (j) } j=1 m ; the homogeneous relations are represented as {Γ (j) } j=1 m ; the heterogeneous relations are represented as {Υ (ij) } i,j=1 m ; the mixed membership model is represented as {Λ (j) } j=1 m ; the latent indicators are represented as {C (j) } j=1 m ; wherein the likelihood function is represented by 
 {tilde over (Ω)}={{{tilde over (Λ)} (j) } j=1 m ,{{tilde over (Θ)} (j) } j=1 m ,{{tilde over (Γ)} (j) } j=1 m ,{{tilde over (Υ)} (ij) } i,j=1 m , wherein the at least one automated processor is further configured to: initialize the likelihood function, and compute the posterior Pr({C (j) }|F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m ,{tilde over (Ω)}) using the Gibbs sampler.