Cross-domain clusterability evaluation for cross-guided data clustering based on alignment between data domains

1. A method for evaluating cross-domain clusterability upon a target domain and a source domain, said method comprising:

calculating, by a processor of a computer system, target clusterability as an average of a respective clusterability of at least one target data item comprised by the target domain;

said processor calculating target-side matchability as an average of a respective matchability of each target centroid of the target domain to source centroids of the source domain, wherein the source domain comprises at least one source data item;

said processor calculating source-side matchability as an average of a respective matchability of each source centroid of said source centroids to the target centroids;

said processor calculating source-target pair matchability as an average of the target-side matchability and the source-side matchability;

said processor calculating cross-domain clusterability between the target domain and the source domain as a linear combination of the calculated target clusterability and the calculated source-target pair matchability by use of a trade-off parameter that indicates a relative contribution of the target clusterability and the source-target pair matchability to the cross-domain clusterability; and

said processor transferring the calculated cross-domain clusterability to a device selected from the group consisting of an output device of a computer system, a storage device of the computer system, a remote computer system coupled to the computer system, and a combination thereof.

2. The method of claim 1 , wherein the target clusterability Cab(T| C t ) is Cab(T| C t )=Σ {t i εT} Cab(t i | C t )/|T|, wherein the respective clusterability Cab(t i | C t ) of an i-th target data item t i of said at least one target data item of the target domain T is Cab(t i | C t )=sim(t i , C * t ))−max { C j t ≠ C * t (t i )} sim(t i , C j t ), for integer i being greater than or equal to 1 and smaller than or equal to k, wherein a first term sim(t i , C j t ) is a respective similarity of the i-th target data item t i to a respective true target centroid of the i-th target data item t i , denoted as C * t (t i ), and wherein a second term max { C j t ≠ C * t (t i )} sim(t i , C j t ) is a largest value among all respective similarities of each target data item t i to a respective target centroid C j t that is not the respective true target centroid C * t (t i ).

3. The method of claim 2 , wherein the target-side matchability Mab( C t | C s ) is Mab( C t | C s )=Σ C i t Mab( C i t | C s )/| C t |, wherein the respective matchability Mab( C i t | C s ) of an i-th target centroid C i t of the target domain to the source centroids C s is Mab( C i t | C s )=sim x ( C i t , C * s ( C i t ))−max { C j s ≠ C * s ( C i t )} sim x ( C i t , C j s ), for integer i being greater than or equal to 1 and smaller than or equal to k, wherein a first term sim x ( C i t , C * s ( C i t )) is a first cross-domain similarity between the i-th target centroid C i t and a nearest source centroid C * s ( C i t ), the first term being the largest value among all similarity values between the i-th target centroid C i t and the source centroids C s , and wherein a second term max { C j s ≠ C * s ( C i t )} sim x ( C i t , C j s ) is a largest value among all cross-domain similarities between the i-th target centroid C i t and a respective source centroid C j s that is not the nearest source centroid C * s ( C i t ).

4. The method of claim 2 , wherein the source-side matchability Mab( C s | C t ) is Mab( C s | C t )=Σ C j s Mab( C j s | C t )/| C s |, wherein the respective matchability Mab( C j s | C t ) of a j-th source centroid C j s of the source domain to the target centroids C t is Mab( C j s | C t )=sim x ( C j s , C * t ( C j s ))−max { C i t ≠ C * t ( C j s )} sim x ( C j s , C i t ), for integer i being greater than or equal to 1 and smaller than or equal to k, wherein a first term sim x ( C j s , C * t ( C j s )) is a first cross-domain similarity between the j-th source centroid C j s and a nearest target centroid C * t ( C j s ), the first term being the largest value among all similarity values between the j-th source centroid C j s and the target centroids C i t , and wherein a second term max { C i t ≠ C * t ( C j s )} sim x ( C j s , C i t ) is a largest value among all cross-domain similarities between the j-th source centroid C j s and a respective target centroid C i t that is not the nearest target centroid C * t ( C j s ).

5. The method of claim 2 , wherein the cross-domain clusterability Cab(T|C s , C t ) between the target domain T and the source domain is Cab(T|C s , C t )=αCab(T|C t )+(1−α)Mab(C s , C t ), wherein the calculated target clusterability Cab(T|C t ) has a value from a first range of [−1.0, +1.0], wherein the calculated source-target pair matchability Mab(C s , C t ) has a value from a second range of [0.0, 1.0], and wherein the trade-off parameter α has a value from a third range of [0.0, 1.0].

6. A computer program product, comprising a computer readable hardware memory having a computer readable program code embodied therein, said computer readable program code containing instructions that perform a method for evaluating cross-domain clusterability upon a target domain and a source domain, said method comprising:

said processor calculating target clusterability as an average of a respective clusterability of said at least one target data item comprised by the target domain;

said processor calculating target-side matchability as an average of a respective matchability of each target centroid of the target domain to source centroids of the source domain, wherein the source domain comprises at least one source data item;

said processor calculating source-side matchability as an average of a respective matchability of each source centroid of said source centroids to the target centroids;

said processor calculating source-target pair matchability as an average of the target-side matchability and the source-side matchability;

said processor calculating cross-domain clusterability between the target domain and the source domain as a linear combination of the calculated target clusterability and the calculated source-target pair matchability by use of a trade-off parameter that indicates a relative contribution of the target clusterability and the source-target pair matchability to the cross-domain clusterability; and

said processor transferring the calculated cross-domain clusterability to a device selected from the group consisting of an output device of a computer system, a storage device of the computer system, a remote computer system coupled to the computer system, and a combination thereof.

7. The computer program product of claim 6 , wherein the target clusterability Cab(T| C t ) is Cab(T| C t )=Σ {t i εT} Cab(t i | C t )/|T|, wherein the respective clusterability Cab(t i | C t ) of an i-th target data item t i of said at least one target data item of the target domain T is Cab(t i | C t )=sim(t i , C * t (t i ))−max { C j t ≠ C * t (t i )} sim(t i , C j t ), for integer i being greater than or equal to 1 and smaller than or equal to k, wherein a first term sim(t i , C j t ) is a respective similarity of the i-th target data item t i to a respective true target centroid of the i-th target data item t i , denoted as C * t (t i ), and wherein a second term max { C j t ≠ C * t (t i )} sim(t i , C j t ) is a largest value among all respective similarities of each target data item t i to a respective target centroid C j t that is not the respective true target centroid C * t (t i ).

8. The computer program product of claim 7 , wherein the target-side matchability Mab( C t | C s ) is Mab( C t | C s )=Σ C i t Mab( C i t | C s )/|C t |, wherein the respective matchability Mab( C i t | C s ) of an i-th target centroid C i t of the target domain to the source centroids C s is Mab( C i t | C s )=sim x ( C i t , C * s ( C i t ))−max { C j s ≠ C * s ( C i t )} for integer i being greater than or equal to 1 and smaller than or equal to k, wherein a first term sim x ( C i t , C * s ( C i t )) is a first cross-domain similarity between the i-th target centroid C i t and a nearest source centroid C * s ( C i t ), the first term being the largest value among all similarity values between the i-th target centroid C i t and the source centroids C s , and wherein a second term max { C j s ≠ C * s ( C i t )} sim x ( C i t , C j s ) is a largest value among all cross-domain similarities between the i-th target centroid C i t and a respective source centroid C j s that is not the nearest source centroid C * s ( C i t ).

9. The computer program product of claim 7 , wherein the source-side matchability Mab( C s | C t ) is Mab( C s | C t )=Σ C j s Mab( C j s | C t )/|C s |, wherein the respective matchability Mab( C j s | C t ) of a j-th source centroid C j s of the source domain to the target centroids C t is Mab( C j s | C t )=sim x ( C j s , C * t ( C j s ))−max { C i t ≠ C * t ( C j s )} sim x ( C j s , C i t ), for integer i being greater than or equal to 1 and smaller than or equal to k, wherein a first term sim x ( C j s , C * t ( C j s )) is a first cross-domain similarity between the j-th source centroid C j s and a nearest target centroid C * t ( C j s ), the first term being the largest value among all similarity values between the j-th source centroid C j s and the target centroids C t , and wherein a second term max { C i t ≠ C * t ( C j s )} sim x ( C j s , C i t ) is a largest value among all cross-domain similarities between the j-th source centroid C j s and a respective target centroid C i t that is not the nearest target centroid C * t ( C j s ).

10. The computer program product of claim 7 , wherein the cross-domain clusterability Cab(T|C s , C t ) between the target domain T and the source domain is Cab(T|C s , C t )=αCab(T|C t )+(1−α)Mab(C s , C t ), wherein the calculated target clusterability Cab(T|C t ) has a value from a first range of [−1.0, +1.0], wherein the calculated source-target pair matchability Mab(C s , C t ) has a value from a second range of [0.0, 1.0], and wherein the trade-off parameter α has a value from a third range of [0.0, 1.0].

11. A computer system comprising a processor and a computer readable memory unit coupled to the processor, said computer readable memory unit containing instructions that when run by the processor implement a method for evaluating cross-domain clusterability upon a target domain and a source domain, said method comprising:

said processor calculating target clusterability as an average of a respective clusterability of said at least one target data item comprised by the target domain;

said processor calculating target-side matchability as an average of a respective matchability of each target centroid of the target domain to source centroids of the source domain, wherein the source domain comprises at least one source data item;

said processor calculating source-side matchability as an average of a respective matchability of each source centroid of said source centroids to the target centroids;

said processor calculating source-target pair matchability as an average of the target-side matchability and the source-side matchability;

said processor calculating cross-domain clusterability between the target domain and the source domain as a linear combination of the calculated target clusterability and the calculated source-target pair matchability by use of a trade-off parameter that indicates a relative contribution of the target clusterability and the source-target pair matchability to the cross-domain clusterability; and

said processor transferring the calculated cross-domain clusterability to a device selected from the group consisting of an output device of a computer system, a storage device of the computer system, a remote computer system coupled to the computer system, and a combination thereof.

12. The computer system of claim 11 , wherein the target clusterability Cab(T| C t ) is Cab(T| C t )=Σ {t i εT} Cab(t i | C t )/|T|, wherein the respective clusterability Cab(t i | C t ) of an i-th target data item t i of said at least one target data item of the target domain T is Cab(t i | C t )=sim(t i , C * t (t i ))−max { C j t ≠ C * t (t i )} sim(t i , C j t ), for integer i being greater than or equal to 1 and smaller than or equal to k, wherein a first term sim(t i , C j t ) is a respective similarity of the i-th target data item t i to a respective true target centroid of the i-th target data item t i , denoted as C * t (t i ), and wherein a second term max { C j t ≠ C * t (t i )} sim(t i , C j t ) is a largest value among all respective similarities of each target data item t i to a respective target centroid C j t that is not the respective true target centroid C * t (t i ).

13. The computer system of claim 12 , wherein the target-side matchability Mab( C t | C s ) is Mab( C t | C s )=Σ C i t Mab( C i t | C s )/|C t |, wherein the respective matchability Mab( C i t | C s ) of an i-th target centroid C i t of the target domain to the source centroids C s is Mab( C i t | C s )=sim x ( C i t , C * s ( C i t ))−max { C j s ≠ C * s ( C i t )} sim x ( C i t , C j s ), for integer i being greater than or equal to 1 and smaller than or equal to k, wherein a first term sim x ( C i t , C * s ( C i t )) is a first cross-domain similarity between the i-th target centroid C i t and a nearest source centroid C * s ( C i t ), the first term being the largest value among all similarity values between the i-th target centroid C i t and the source centroids C s , and wherein a second term max { C j s ≠ C * s ( C i t )} sim x ( C i t , C j s ) is a largest value among all cross-domain similarities between the i-th target centroid C i t and a respective source centroid C j s that is not the nearest source centroid C * s ( C i t ).

14. The computer system of claim 12 , wherein the source-side matchability Mab( C s | C t ) is Mab( C s | C t )=Σ C j s Mab( C j s | C t )/|C s |, wherein the respective matchability Mab( C j s | C t ) of a j-th source centroid C j s of the source domain to the target centroids C t is Mab( C j s | C t )=sim x ( C i s , C * t ( C j s ))−max { C i t ≠ C * t ( C j s )} sim x ( C j s , C i t ), for integer i being greater than or equal to 1 and smaller than or equal to k, wherein a first term sim x ( C j s , C * t ( C j s )) is a first cross-domain similarity between the j-th source centroid C j s and a nearest target centroid C * t ( C j s ), the first term being the largest value among all similarity values between the j-th source centroid C j s and the target centroids C t , and wherein a second term max { C i t ≠ C * t ( C j s )} sim x ( C j s , C i t ) is a largest value among all cross-domain similarities between the j-th source centroid C j s and a respective target centroid C i t that is not the nearest target centroid C * t ( C j s ).

15. The computer system of claim 12 , wherein the cross-domain clusterability Cab(T|C s , C t ) between the target domain T and the source domain is Cab(T|C s , C t )=αCab(T|C t )+(1−α)Mab(C s , C t ), wherein the calculated target clusterability Cab(T|C t ) has a value from a first range of [−1.0, +1.0], wherein the calculated source-target pair matchability Mab(C s , C t ) has a value from a second range of [0.0, 1.0], and wherein the trade-off parameter α has a value from a third range of [0.0, 1.0].