Patent ID: 8055592

Claim:
A method for unsupervised clustering data objects, comprising: calculating, with a processor, based on a relative depth in a semantic hierarchical tree of a dictionary, an importance value of at least one member in a first data object represented as a variable length vector of 0 to N members, said vector further comprising a subset of said members having an importance value above a designated importance threshold, wherein the data objects comprise sentences and said members comprise words, therein; calculating, with said processor, based on a path distance in said semantic hierarchical tree of a dictionary, a member similarity value for each member of said subset of said members to at least a second data object; when none of said subset of said members of said first data object are associated with at least one of a subset of members of said at least a second data object, in dependence upon a comparison of similarity values, dynamically form, with a clustering module, a first cluster comprising said first data object; and when at least one of said subset of said members of said first data object is associated with at least one of a subset of members of said at least a second data object, in dependence upon a comparison of similarity values, dynamically form, with said clustering module, at least a second cluster comprising said first data object and said at least a second data object.