16. A method for relational analysis of data, comprising: (a) receiving a data set comprising a plurality of interrelated data objects each having at least one type of data associated with a plurality of latent classes, and having at least one of respective data object attributes, homogeneous relations between the respective data object and data objects having the same type, and heterogeneous relations between the respective data object and data objects having different types; (b) generating latent indicators for the plurality of interrelated data objects, comprising respective latent class membership parameters, based on at least the respective data object attributes, available homogeneous relations between the respective data object and data objects having the same type, and available heterogeneous relations between the respective data object and data objects having different types, 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; (c) optimizing a probabilistic function comprising a mixed membership relational clustering model which estimates a joint probability distribution over the generated latent indicators of the data set and observations of the respective data object attributes; (d) clustering the data set with at least one automated processor, using the optimized probabilistic function, based on at least the data object attributes, the homogeneous relations and the heterogeneous relations; and (d) storing information representing the clustering of the plurality of the data set.
16. A method for relational analysis of data, comprising: (a) receiving a data set comprising a plurality of interrelated data objects each having at least one type of data associated with a plurality of latent classes, and having at least one of respective data object attributes, homogeneous relations between the respective data object and data objects having the same type, and heterogeneous relations between the respective data object and data objects having different types; (b) generating latent indicators for the plurality of interrelated data objects, comprising respective latent class membership parameters, based on at least the respective data object attributes, available homogeneous relations between the respective data object and data objects having the same type, and available heterogeneous relations between the respective data object and data objects having different types, 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; (c) optimizing a probabilistic function comprising a mixed membership relational clustering model which estimates a joint probability distribution over the generated latent indicators of the data set and observations of the respective data object attributes; (d) clustering the data set with at least one automated processor, using the optimized probabilistic function, based on at least the data object attributes, the homogeneous relations and the heterogeneous relations; and (d) storing information representing the clustering of the plurality of the data set. 20. The method according to claim 16 , further comprising maximizing the probabilistic function by iteratively updating {tilde over (Ω)}=Ω over the range of i and j, until convergence is achieved, to estimate unknown parameters of a joint probability distribution equation over the observations and latent indicators, with update rules selected from the group consisting of update rule set (I) and update rule set (II): update rule set (I) Λ hp (1) =PR(C hp (1) =1|F,S,R,{tilde over (Ω)}), Θ · g = ∑ p = 1 n 1 ⁢ F · p ⁢ Pr ⁡ ( C gp ( 1 ) = 1 , ❘ F , S , R , Ω ~ ) ∑ p = 1 n 1 ⁢ Pr ⁡ ( C gp ( 1 ) = 1 , ❘ F , S , R , Ω ~ ) Γ gh = ∑ p , q = 1 n 1 ⁢ S pq ⁢ Pr ⁡ ( C gp ( 1 ) = 1 , C hq ( 1 ) = 1 ❘ F , S , R , Ω ~ ) ∑ p , q = 1 n 1 ⁢ Pr ⁡ ( C gp ( 1 ) = 1 , C hq ( 1 ) = 1 ❘ F , S , R , Ω ~ ) , update rule set (II) update {C (j) } j=1 m , to maximize the objective function log ⁢ ⁢ L ⁡ ( Ω ❘ Ψ ) = ∑ p = 1 n 1 ⁢ ∑ g = 1 k 1 ⁢ C gp ( 1 ) ⁢ log ⁢ ⁢ Λ gp ( 1 ) + ∑ q = 1 n 2 ⁢ ∑ h = 1 k 2 ⁢ C hq ( 2 ) ⁢ log ⁢ ⁢ Λ gp ( 2 ) , 
 − D φ 1 ( F,ΘC (1) )− D φ 2 ( S, ( C (1) ) T ΓC (1) )− D φ 3 ( R,C (1) ) T ΥC (2) +τ wherein Θ · g ( j ) = ∑ p : C gp ( j ) = 1 ⁢ F · p ( j ) ∑ p = 1 n j ⁢ C gp ( j ) , Γ gh ( j ) = ∑ p : C gp ( i ) = 1 , q : C hq ( j ) = 1 ⁢ S pq ( j ) ∑ p = 1 n j ⁢ C gp ( j ) ⁢ ∑ q = 1 n j ⁢ C hq ( j ) , Υ gh ( ij ) = ∑ p : C gp ( i ) = 1 , q : C hq ( j ) = 1 ⁢ R pq ( ij ) ∑ p = 1 n i ⁢ C gp ( i ) ⁢ ∑ q = 1 n j ⁢ C hq ( j ) , Ψ={{C (j) } j=1 m , {F (j) } j=1 m , {R (ij) } i,j=1 m }, τ=log f φ 1 (F)+log f φ 2 (S)+log f φ 3 (R), D φ 1 is a Bregman divergence for the attributes, D φ 2 is a Bregman divergence for the homogeneous relations, D φ 3 is a Bregman divergence for heterogeneous relations, and f φ n represents a uniquely determined function for each respective exponential family distribution.