A method includes obtaining, at one or more processors, data comprising multiple variables corresponding to multiple samples in a very large dataset, defining, via the one or more processors, multiple sets of variables occurring in the samples comprising a set of x variables and a set of y variables, where the intersection of the sets is zero, for each set of variables, determining, via the one or more processors, a support for each set and a union of each set, determining, via the one or more processors, an interest for each of the multiple association rules of the sets of variables, and determining, via the one or more processors, a chi squared interest, (χ2 interest), for each association to identify related sets of variables, including almost exclusive relationships.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: obtaining, at one or more processors, data comprising multiple variables corresponding to multiple samples in a very large dataset; defining, via the one or more processors, multiple sets of variables occurring in the samples comprising a set of x variables and a set of y variables, where the intersection of the sets is zero; for each set of variables, determining, via the one or more processors, a support for each set and a union of each set; determining, via the one or more processors, an interest for each of the multiple association rules of the sets of variables; and determining, via the one or more processors, a chi squared interest, (χ2 interest), for each association to identify related sets of variables, including almost exclusive relationships. 2. The method of claim 1 wherein x∪y is denoted by xy, if x∩y=0 and wherein the chi-squared interest is stored in a memory in association with each variable. 3. The method of claim 2 wherein support for x is defined as supp(x)=Nx/n, where Nx is the number of observations of x in a sample with size n. 4. The method of claim 3 wherein support for y is defined as supp(y)=Ny/n, where Ny is the number of observations of y in a sample with size n. 5. The method of claim 4 wherein for any association rule of xy, its confidence conf(xy)=supp(xy)/supp(x). 6. The method of claim 5 wherein the χ2-interest of a rule xy is defined by: χ 2 = 2  n · supp  ( x ⊎ y )  ln   interest  ( x ⇒ y ) + 2  n · supp _  ( x ⊎ y )  ln   interest _  ( x ⇒ y ) ,   where   supp _  ( x ⊎ y ) = 1 - supp  ( x ⊎ y ) = 1 - N x ⊎ y / n , and  interest _  ( x ⇒ y ) = 1 - N x ⊎ y / n 1 - N x  N y / n 2 . 7. The method of claim 5 wherein a combination of high χ2-interest with a low confidence is representative of an almost exclusive relationship. 8. The method of claim 7 wherein conf(xy)>0:05 is indicative of a positive association between x and y where the χ2-interest is high. 9. The method of claim 1 and further comprising generating a graphical output having lines drawn between associations of each set of variables, wherein the sets of variable are generally arranged in a circle with the length of the lines connecting the sets of variables being proportional to the χ2-interest between the sets of variables. 10. A computer implemented system comprising: a non-transitory memory storage comprising instructions; and one or more processors in communication with the memory, wherein the one or more processors execute the instructions to: obtain, via the one or more processors, data comprising multiple variables corresponding to multiple samples in a very large dataset; define, via the one or more processors, multiple sets of variables occurring in the samples comprising a set of x variables and a set of y variables, where the intersection of the sets is zero; for each set of variables, determine, via the one or more processors, a support for each set and a union of each set; determine, via the one or more processors, an interest for each of the multiple association rules of the sets of variables; and determine, via the one or more processors, a chi squared interest, (χ2 interest), for each association to identify related sets of variables, including almost exclusive relationships. 11. The system of claim 10 wherein x∪y is denoted by xy, if x∩y=0, support for x is defined as supp(x)=Nx/n, where Nx is the number of observations of x in a sample with size n, and support for y is defined as supp(y)=Ny/n, where Ny is the number of observations of y in a sample with size n. 12. The system of claim 11 wherein for any association rule of xy, its confidence conf(xy)=supp(xy)/supp(x). 13. The system of claim 12 wherein the χ2-interest of a rule xy is defined by: χ 2 = 2  n · supp  ( x ⊎ y )  ln   interest  ( x ⇒ y ) + 2  n · supp _  ( x ⊎ y )  ln   interest _  ( x ⇒ y ) ,   where   supp _  ( x ⊎ y ) = 1 - supp  ( x ⊎ y ) = 1 - N x ⊎ y / n , and  interest _  ( x ⇒ y ) = 1 - N x ⊎ y / n 1 - N x  N y / n 2 . 14. The system of claim 13 wherein a combination of high χ2-interest with a low confidence is representative of an almost exclusive relationship and wherein conf(xy)>0:05 is indicative of a positive association between x and y where the χ2-interest is high. 15. The system of claim 10 further comprising a display device coupled to the processor, wherein the operations further comprise generating a graphical output for display on the display device having lines drawn between associations of each set of variables, wherein the sets of variable are generally arranged in a circle with the length of the lines connecting the sets of variables being proportional to the χ2-interest between the sets of variables. 16. A non-transitory computer readable media storing computer instructions that when executed by one or more processors, cause the one or more processors to perform the steps of: obtaining, via the one or more processors, data comprising multiple variables corresponding to multiple samples in a very large dataset; defining, via the one or more processors, multiple sets of variables occurring in the samples comprising a set of x variables and a set of y variables, where the intersection of the sets is zero; for each set of variables, determining, via the one or more processors, a support for each set and a union of each set; determining, via the one or more processors, an interest for each of the multiple association rules of the sets of variables; and determining, via the one or more processors, a chi squared interest, (χ2 interest), for each association to identify related sets of variables, including almost exclusive relationships. 17. The non-transitory computer readable storage media of claim 16 wherein x∪y is denoted by xy, if x∩y=0, support for x is defined as supp(x)=Nx/n, where Nx is the number of observations of x in a sample with size n, support for y is defined as supp(y)=Ny/n, where Ny is the number of observations of y in a sample with size n, and wherein for any association rule of xy, its confidence conf(xy)=supp(xy)/supp(x). 18. The non-transitory computer readable storage media of claim 17 wherein the χ2-interest of a rule xy is defined by: χ 2 = 2  n · supp  ( x ⊎ y )  ln   interest  ( x ⇒ y ) + 2  n · supp _  ( x ⊎ y )  ln   interest _  ( x ⇒ y ) ,   where   supp _  ( x ⊎ y ) = 1 - supp  ( x ⊎ y ) = 1 - N x ⊎ y / n , and  interest _  ( x ⇒ y ) = 1 - N x ⊎ y / n 1 - N x  N y / n 2 . 19. The non-transitory computer readable storage media of claim 18 wherein a combination of high χ2-interest with a low confidence is representative of an almost exclusive relationship and wherein conf(xy)>0:05 is indicative of a positive association between x and y where the χ2-interest is high. 20. The non-transitory computer readable storage media of claim 16 wherein the operations further comprise generating a graphical output for a display device having lines drawn between associations of each set of variables, wherein the sets of variable are generally arranged in a circle with the length of the lines connecting the sets of variables being proportional to the χ2-interest between the sets of variables.