Generating data clusters
Techniques are disclosed for prioritizing a plurality of clusters. Prioritizing clusters may generally include identifying a scoring strategy for prioritizing the plurality of clusters. Each cluster is generated from a seed and stores a collection of data retrieved using the seed. For each cluster, elements of the collection of data stored by the cluster are evaluated according to the scoring strategy and a score is assigned to the cluster based on the evaluation. The clusters may be ranked according to the respective scores assigned to the plurality of clusters. The collection of data stored by each cluster may include financial data evaluated by the scoring strategy for a risk of fraud. The score assigned to each cluster may correspond to an amount at risk.
1. A computer-implemented method comprising:
by one or more hardware computer processors configured with specific computer executable instructions:
executing a cluster engine configured to at least:
access one or more electronic data stores, the one or more electronic data stores storing a plurality of data entities and respective data entity attributes;
designate a seed data entity, from the plurality of data entities, as the data entity cluster;
perform first growth of the data entity cluster by executing at least a first search protocol on the one or more electronic data stores to identify one or more data entities;
perform second growth of the data entity cluster by executing at least a second search protocol on the one or more electronic data stores to identify one or more additional data entities, the second search protocol different from the first search protocol;
store the data entity cluster in at least one of the one or more electronic data stores; and
determine scores for the data entity cluster and a plurality of additional data entity clusters generated based on additional seed data entities; and
executing a workflow engine configured to at least:
cause presentation of the data entity cluster and the plurality of additional data entity clusters in a cluster analysis user interface; and
order the presented data entity cluster and the plurality of additional data entity clusters in the cluster analysis user interface based at least in part on the respective determined scores for the data entity cluster and the plurality of additional data entity clusters.
2. The computer-implemented method of claim 1 , wherein executing the first search protocol on the one or more electronic data stores to identify one or more data entities comprises:
by the one or more hardware computer processors configured with specific computer executable instructions:
identifying at least one data entity attribute associated with the seed data entity; and
evaluating the plurality of data entities to determine the one or more data entities sharing the at least one data entity attribute with the seed data entity.
3. The computer-implemented method of claim 2 , wherein executing the first search protocol on the one or more electronic data stores to identify one or more data entities further comprises:
by the one or more hardware computer processors configured with specific computer executable instructions:
applying a filter to the at least one data entity attribute associated with the seed data entity, the filter selected based on a clustering strategy.
4. The computer-implemented method of claim 1 further comprising:
by the one or more hardware computer processors configured with specific computer executable instructions:
executing the cluster engine further configured to at least:
compare data entities associated with the data entity cluster to data entities associated with a second data entity cluster; and
in response to determining that at least one data entity associated with the data entity cluster shares an attribute with and/or is related to at least one data entity associated with the second data entity cluster, merge the data entity cluster and the second data entity cluster.
5. The computer-implemented method of claim 1 , wherein the first search protocol searches for data entities in a first electronic data store and the second search protocol searches for data entities in a second electronic data store.
6. The computer-implemented method of claim 1 , further comprising:
by the one or more hardware computer processors configured with specific computer executable instructions:
executing the cluster engine further configured to at least:
iteratively generate the data entity cluster by at least performing third growth of the data entity cluster by executing at least a third search protocol on the one or more electronic data stores to identify yet one or more additional data entities related to the one or more additional data entities.
7. The computer-implemented method of claim 1 , wherein determining the score for the data entity cluster comprises:
determining a plurality of base scores for the data entity cluster;
determining, based on the plurality of base scores, an overall score for the data entity cluster; and
assigning the overall score to the data entity cluster as the score.
8. The computer-implemented method of claim 7 , wherein the score is based on a scoring strategy, and the score corresponds to an amount of risk.
9. The computer-implemented method of claim 1 further comprising:
by the one or more hardware computer processors configured with specific computer executable instructions:
executing the workflow engine further configured to at least:
generate the cluster analysis user interface configured to be rendered on a computing device; and
receive, via the cluster analysis user interface, a selection of at least one of:
the seed data entity selected from the plurality of data entities, or
a seed generation strategy by which the seed data entity is designated from the plurality of data entities.
10. A system comprising:
a computer readable storage medium having program instructions embodied therewith; and
one or more processors configured to execute the program instructions to cause the system to:
execute a cluster engine configured to at least:
access one or more electronic data stores, the one or more electronic data stores storing a plurality of data entities and respective data entity attributes;
designate a seed data entity, from the plurality of data entities, as the data entity cluster;
perform first growth of the data entity cluster by executing at least a first search protocol on the one or more electronic data stores to identify one or more data entities;
perform second growth of the data entity cluster by executing at least a second search protocol on the one or more electronic data stores to identify one or more additional data entities, the second search protocol different from the first search protocol;
store the data entity cluster in at least one of the one or more electronic data stores; and
determine scores for the data entity cluster and a plurality of additional data entity clusters generated based on additional seed data entities; and
execute a workflow engine configured to at least:
cause presentation of the data entity cluster and the plurality of additional data entity clusters in a cluster analysis user interface; and
order the presented data entity cluster and the plurality of additional data entity clusters in the cluster analysis user interface based at least in part on the respective determined scores for the data entity cluster and the plurality of additional data entity clusters.
11. The system of claim 10 , wherein executing the first search protocol on the one or more electronic data stores to identify one or more data entities comprises:
identifying at least one data entity attribute associated with the seed data entity; and
evaluating the plurality of data entities to determine the one or more data entities sharing the at least one data entity attribute with the seed data entity.
12. The system of claim 11 , wherein executing the first search protocol on the one or more electronic data stores to identify one or more data entities further comprises:
applying a filter to the at least one data entity attribute associated with the seed data entity, the filter selected based on a clustering strategy.
13. The system of claim 10 , wherein the one or more processors are configured to execute the program instructions to further cause the system to:
execute the cluster engine further configured to at least:
compare data entities associated with the data entity cluster to data entities associated with a second data entity cluster; and
in response to determining that at least one data entity associated with the data entity cluster shares an attribute with and/or is related to at least one data entity associated with the second data entity cluster, merge the data entity cluster and the second data entity cluster.
14. The system of claim 10 , wherein the first search protocol searches for data entities in a first electronic data store and the second search protocol searches for data entities in a second electronic data store.
15. The system of claim 10 , wherein the one or more processors are configured to execute the program instructions to further cause the system to:
execute the cluster engine further configured to at least:
iteratively generate the data entity cluster by at least performing third growth of the data entity cluster by executing at least a third search protocol on the one or more electronic data stores to identify yet one or more additional data entities related to the one or more additional data entities.
16. The system of claim 10 , wherein determining the score for the data entity cluster comprises:
determining a plurality of base scores for the data entity cluster;
determining, based on the plurality of base scores, an overall score for the data entity cluster; and
assigning the overall score to the data entity cluster as the score.
17. The system of claim 10 , wherein the one or more processors are configured to execute the program instructions to further cause the system to:
execute the workflow engine further configured to at least:
generate the cluster analysis user interface configured to be rendered on a computing device; and
receive, via the cluster analysis user interface, a selection of at least one of:
the seed data entity selected from the plurality of data entities, or
a seed generation strategy by which the seed data entity is designated from the plurality of data entities.
18. A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to:
execute a cluster engine configured to at least:
access one or more electronic data stores, the one or more electronic data stores storing a plurality of data entities and respective data entity attributes;
designate a seed data entity, from the plurality of data entities, as the data entity cluster;
perform first growth of the data entity cluster by executing at least a first search protocol on the one or more electronic data stores to identify one or more data entities;
perform second growth of the data entity cluster by executing at least a second search protocol on the one or more electronic data stores to identify one or more additional data entities, the second search protocol different from the first search protocol;
store the data entity cluster in at least one of the one or more electronic data stores; and
determine scores for the data entity cluster and a plurality of additional data entity clusters generated based on additional seed data entities; and
execute a workflow engine configured to at least:
cause presentation of the data entity cluster and the plurality of additional data entity clusters in a cluster analysis user interface; and
order the presented data entity cluster and the plurality of additional data entity clusters in the cluster analysis user interface based at least in part on the respective determined scores for the data entity cluster and the plurality of additional data entity clusters.