Generating data clusters
First Claim
1. A computer-implemented method comprising:
- by one or more hardware computer processors configured with specific computer executable instructions;
accessing one or more electronic data stores, the one or more electronic data stores storing a plurality of data entities and respective data entity attributes;
applying a clustering strategy to generate a data entity cluster by at least;
designating a seed data entity, from the plurality of data entities, as the data entity cluster;
accessing, based on the clustering strategy, one or more search protocols;
performing first growth of the data entity cluster by executing at least a first of the one or more search protocols on the one or more electronic data stores to identify one or more data entities related to the seed data entity;
adding the one or more data entities to the data entity cluster;
performing second growth of the data entity cluster by executing at least a second of the one or more search protocols on the one or more electronic data stores to identify one or more additional data entities related to the one or more added data entities, the second search protocol different than the first search protocol; and
adding the one or more additional data entities to the data entity cluster; and
storing the data entity cluster in at least one of the one or more electronic data stores.
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Accused Products
Abstract
Techniques are disclosed for 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.
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Citations
20 Claims
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1. A computer-implemented method comprising:
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by one or more hardware computer processors configured with specific computer executable instructions; accessing one or more electronic data stores, the one or more electronic data stores storing a plurality of data entities and respective data entity attributes; applying a clustering strategy to generate a data entity cluster by at least; designating a seed data entity, from the plurality of data entities, as the data entity cluster; accessing, based on the clustering strategy, one or more search protocols; performing first growth of the data entity cluster by executing at least a first of the one or more search protocols on the one or more electronic data stores to identify one or more data entities related to the seed data entity; adding the one or more data entities to the data entity cluster; performing second growth of the data entity cluster by executing at least a second of the one or more search protocols on the one or more electronic data stores to identify one or more additional data entities related to the one or more added data entities, the second search protocol different than the first search protocol; and adding the one or more additional data entities to the data entity cluster; and storing the data entity cluster in at least one of the one or more electronic data stores. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-implemented method of accessing one or more electronic data sources, the method comprising:
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by one or more hardware computer processors configured with specific computer executable instructions; accessing one or more electronic data stores, the one or more electronic data stores storing; a plurality of data entities and respective data entity attributes, and a plurality of data entity clusters; and causing access of a data entity cluster of the plurality of data entity clusters, wherein the data entity cluster is related to a clustering strategy, and wherein the data entity cluster has been iteratively generated by; designating a seed data entity, from the plurality of data entities, as the data entity cluster; accessing, based on the clustering strategy, one or more search protocols; performing first growth of the data entity cluster by executing at least a first of the one or more search protocols on the one or more electronic data stores to identify one or more data entities related to the seed data entity; adding the one or more data entities to the data entity cluster; performing second growth of the data entity cluster by executing at least a second of the one or more search protocols on the one or more electronic data stores to identify one or more additional data entities related to the one or more added data entities, the second search protocol different than the first search protocol; and adding the one or more additional data entities to the data entity cluster. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification