1. A method of unsupervised classification of a biological dataset comprising the steps of: a. Obtaining a biological dataset, said biological dataset defining a scene b. Obtaining a prior distribution of a feature in a class, wherein each class is previously defined for the dataset; c. Computing a combined posterior likelihood that each feature belongs to the class; d. Using graph embedding (GEM), assigning each feature to an embedded space, wherein the assignment is based on the posterior likelihood; e. Partitioning the features in the embedded space to disjointed regions, thereby creating classes; and f. Generating new distribution for each feature in the new classes, whereupon comparison to the prior likelihood distribution, a classification is made.