Patent ID: 7720848
Filing Date: 2010-05-18
Classification: G06F,G06K,Y10S

Abstract:
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: wherein the local updating operation is performed by a processor configured to perform the local updating.