Self-learning automated information technology change risk prediction
US-2024414064-A1 · Dec 12, 2024 · US
US8949237B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-8949237-B2 |
| Application number | US-201213345593-A |
| Country | US |
| Kind code | B2 |
| Filing date | Jan 6, 2012 |
| Priority date | Jan 6, 2012 |
| Publication date | Feb 3, 2015 |
| Grant date | Feb 3, 2015 |
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A technique for identifying overlapping clusters of items in a data set. The technique may be used in connection with a social network or other on-line environment in which users express approval for other users, such as through votes, tags or other inputs. These expressions of approval may be used to form clusters such that entities assigned to a cluster have a higher metric of approval from other entities within the cluster than from outside the cluster. Such clusters may be arrived at through a computationally efficient approach that involves randomly selecting one or more entities as a seed for a cluster. The cluster may be grown by testing other entities, similar to those already in the cluster, to determine whether they are more preferred by those already in the cluster than those outside the cluster. Once a cluster is grown to a desired size, it may be pruned.
Opening claim text (preview).
What is claimed is: 1. At least one computer-readable memory, which is not a signal, comprising computer-executable instructions that, when executed by at least one processor, perform a method, the method comprising acts of: receiving inputs relating to a plurality of entities in a set, each input indicating approval of an entity in the set for another entity in the set; maintaining a database storing indications of approval associated with each of the plurality of entities in t…
Physics · mapped topic
Physics · mapped topic
Physics · mapped topic
Physics · mapped topic
Physics · mapped topic
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