Apparatus and method for determining cluster pairs

1. An apparatus for determining cluster pairs, the apparatus comprising:

a processor; and

a memory communicatively connected to the processor, the memory containing instructions configuring the processor to:

receive a first attribute cluster comprising user data relating to a user's collaboration capabilities;

classify the first attribute cluster to one or more descriptors;

generate a first cluster profile as a function of the classification and the first attribute cluster comprising elements of the first attribute cluster including the user data, wherein the first cluster profile comprises a degree of intensity associated with each descriptor of the one or more descriptors, wherein generating the first cluster profile comprises determining a weighting for each element within the first attribute cluster using a weighting machine learning model, wherein use of the weighting machine learning model results in improved accuracy in selection of data contained in the first cluster profile; and

determine a complementary cluster profile as a function of the first cluster profile comprising associated elements comprising a collaboration skill that complements the first cluster profile, wherein determining the complementary cluster profile comprises:

receiving a plurality of complementary cluster profiles from a database;

comparing each complementary cluster profile of the plurality of cluster profiles with the first cluster profile, wherein the comparison comprises identifying the collaboration skill that complements the first cluster profile; and

selecting one complementary cluster profile from the plurality of complementary cluster profiles based on the comparison of each complementary cluster profile of the plurality of cluster profiles with the first cluster profile.

2. The apparatus of claim 1 , wherein receiving the first attribute cluster comprises receiving the first attribute cluster using a web crawler, wherein the web crawler is configured to generate a search query as a function of search criteria received from the user.

3. The apparatus of claim 1 , wherein classifying the first attribute cluster to one or more descriptors comprises:

generating a descriptor classifier using training data wherein the training data comprises a plurality of first attribute clusters correlated to a plurality of descriptors; and

classifying the first attribute cluster using the descriptor classifier.

4. The apparatus of claim 1 , wherein the degree of intensity comprises a number of elements within the first attribute cluster classified with each descriptor of the one or more descriptors.

5. The apparatus of claim 1 , wherein the processor is further configured to create a user interface data structure as a function of the first cluster profile and the complementary cluster profile.

6. The apparatus of claim 1 , wherein the weighting machine learning model is trained using weighting training data comprising a plurality of elements of first attribute clusters correlated to a plurality of weightings.

7. The apparatus of claim 1 , wherein generating the first cluster profile comprises:

receiving cluster training data comprising a plurality of classified first attribute clusters correlated to a plurality of first cluster profiles;

training a cluster machine learning model as a function of the cluster training data; and

generating the first cluster profile as a function of the cluster machine learning model.

8. The apparatus of claim 1 , wherein receiving the first attribute cluster comprises receiving the first attribute cluster through a graphical user interface.

9. The apparatus of claim 1 , wherein the plurality of complementary cluster profiles comprise a plurality of previous cluster profiles.

10. The apparatus of claim 1 , wherein determining the complementary cluster profile as a function of the first cluster profile comprises determining a complementary cluster profile as a function of a complementary machine learning model.

11. A method for determining cluster pairs, the method comprising:

receiving, by at least a processor, a first attribute cluster comprising user data relating to a user's collaboration capabilities;

classifying, by the at least a processor, the first attribute cluster to one or more descriptors;

generating, by the at least a processor, a first cluster profile as a function of the classification and the first attribute cluster comprising elements of the first attribute cluster including the user data, wherein the first cluster profile comprises a degree of intensity of each descriptor of the one or more descriptors, wherein generating the first cluster profile comprises determining a weighting for each element within the first attribute cluster using a weighting machine learning model, wherein use of the weighting machine learning model results in improved accuracy in selection of data contained in the first cluster profile; and

determining, by the at least a processor, a complementary cluster profile as a function of the first cluster profile comprising associated elements comprising a collaboration skill that complements the first cluster profile and further comprising:

receiving a plurality of complementary cluster profiles from a database;

comparing each complementary cluster profile of the plurality of cluster profiles with the first cluster profile, wherein the comparison comprises identifying the collaboration skill that complements the first cluster profile; and

selecting one complementary cluster profile from the plurality of complementary cluster profiles based on the comparison of each complementary cluster profile of the plurality of cluster profiles with the first cluster profile.

12. The method of claim 11 , wherein receiving, by the at least a processor, the first attribute cluster comprises receiving, by the at least a processor, the first attribute cluster using a web crawler, wherein the web crawler is configured to generate a search query as a function of search criteria received from the user.

13. The method of claim 11 , wherein classifying, by the at least a processor, the first attribute cluster to one or more descriptors comprises:

generating a descriptor classifier using training data, wherein the training data comprises a plurality of first attribute clusters correlated to a plurality of descriptors; and

classifying the first attribute cluster using the descriptor classifier.

14. The method of claim 11 , wherein the degree of intensity comprises a number of elements within the first attribute cluster classified with each descriptor of the one or more descriptors.

15. The method of claim 14 , further comprising creating, by the at least a processor, a user interface data structure as a function of the first cluster profile and the complementary cluster profile.

16. The method of claim 11 , wherein generating, by the at least a processor, wherein the weighting machine learning model is trained using weighting training data comprising a plurality of elements of first attribute clusters correlated to a plurality of weightings.

17. The method of claim 11 , wherein generating, by the at least a processor, the first cluster profile comprises:

receiving cluster training data comprising a plurality of classified first attribute clusters correlated to a plurality of first cluster profiles;

training a cluster machine learning model as a function of the cluster training data; and

generating the first cluster profile as a function of the cluster machine learning model.

18. The method of claim 11 , wherein receiving, by the at least a processor, the first attribute cluster comprises receiving the first attribute cluster by a graphical user interface.

19. The method of claim 11 , wherein the plurality of complementary cluster profiles comprise a plurality of previous cluster profiles.

20. The method of claim 11 , determining, by the at least a processor, a complementary cluster profile as a function of the first cluster profile comprises determining a complementary cluster profile as a function of a complementary machine learning model.