1. A method for updating a probabilistic clustering system defined at least in part by probabilistic model parameters indicative of word counts, ratios, or frequencies characterizing classes of the clustering system, wherein the probabilistic clustering system includes a hierarchy of classes with documents assigned to leaf classes of the hierarchy, the method comprising: changing an association of one or more documents from one or more source classes to one or more destination classes; and locally updating probabilistic model parameters characterizing classes affected by the changed association without updating probabilistic model parameters characterizing classes not affected by the changed association; wherein the changing of the association comprises creating two or more split leaf classes and performing clustering training to associate each document of a pre-existing leaf class with one of the two or more split leaf classes, the cluster training being limited to documents associated with the pre-existing leaf class, the cluster training generating local probabilistic model parameters for characterizing the split leaf classes respective to the documents associated with the pre-existing leaf class, and the changing of the association further comprises one of: (i) replacing the pre-existing leaf class with the two or more split leaf classes in the hierarchy of classes, said documents being associated with the two or more split leaf classes by the cluster training, and (ii) associating each split leaf class with the pre-existing leaf class such that the pre-existing leaf class is the immediate ancestor of each split leaf class, and removing all documents from the pre-existing leaf class, said documents being associated with the two or more split leaf classes by the cluster training; and wherein the local updating operation is performed by a processor configured to perform the local updating.
3. The method as set forth in claim 1 , wherein the changing of the association further comprises: (i) defining a plurality of documents as a document group that is to be kept together, each document of the document group being represented as a bag-of-words and being associated with the pre-existing leaf class; (ii) prior to the performing of cluster training, replacing the plurality of documents of the document group with a temporary document having a bag-of-words representation that combines word counts of the documents of the document group such that the subsequent cluster training associates the temporary document with one of the split leaf classes; (iii) after the performing of the cluster training, replacing the temporary document with the plurality of documents of the document group, each of the plurality of documents being associated with the same split leaf class with which the temporary document was associated by the cluster training.