Patent ID: 7430550

Claim:
A computer-implemented system that facilitate estimation of co-occurrence counts, comprising: a memory and a processor; a receiving component that receives relational data representative of at least one object and at least one target feature and one additional feature; an indexing component that creates first and second indexes based on the relational data, the first index utilized to determine objects that have at least one feature that does not have a default value, or does not have a specific non-default value and the second index utilized to determine features with a non-default value for a given object; a sampling process component that utilizes the first and second indexes to determine at least one sampled co-occurrence for at least one feature and object of interest, the determination of the sampled co-occurrence excludes features with default values; and a co-occurrence count component that utilizes the sampled co-occurrence to estimate an overall co-occurrence count in a population between a given target feature and other features of interest, wherein estimating the overall co-occurrence count comprises at least one of the following: utilizing C ij =S ij *(N/MAXID), where C ij represents an estimation of an actual co-occurrence count in a population and/or fraction of a population, S ij represents a count in a sample for feature F i having a value of 1 and feature F j having a value of 1, N represents a total number of objects, and MAXID represents a number of samples needed to obtain k objects that have non-default valued F i features; utilizing C ij =S ij *(N(F i =1)/k), where C ij represents an estimation of an actual co-occurrence count in a population and/or fraction of a population, S ij represents a count in a sample for feature F i having a value of 1 and feature F j having a value of 1, N represents a total number of objects, and k represents a number of objects needed to obtain a number of samples, MAXID, which have non-default valued F i features; or employing a Hypergeometric likelihood process that utilizes a C ij selected to be a value that maximizes Eq. 1: ( N ( F i = 1 , F j = 1 C ij ) ⁢ ( N ⁡ ( F i = 1 , F j = 0 ) K - C ij ) ( N ⁡ ( F i = 1 ) k ) .