System and method for dynamically clustering data items
First Claim
1. A computer-implemented method for dynamically clustering data items, the method comprising:
- receiving (a) a plurality of data items originating from at least two sources;
(b) a plurality of distinct metadata details;
(c) data indicative of associations between said data items and said metadata details, wherein each data item is associated with at least one metadata detail, and wherein at least a first data item originating from a first source and a second data item originating from a second source are related data items associated with at least one shared metadata detail;
grading strengths of relationships between at least one of said data items and at least one of said metadata details; and
clustering said data items into one or more clusters, based on the calculated grades wherein at least one of said clusters comprises related data items originating from more than one source,wherein said grading comprises applying weighting functions and the weighting functions are rule-based weighting functions.
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Accused Products
Abstract
A method for dynamically clustering data items, the method comprising: receiving a plurality of data items originating from at least two sources, a plurality of distinct metadata details, and data indicative of associations between the data items and the metadata details, wherein each data item is associated with at least one metadata detail indicative of its owner, and wherein at least a first data item originating from a first source and a second data item originating from a second source are related data items associated with at least one shared metadata detail; grading probabilities of relationships between at least one of the data items and at least one of the metadata details; clustering the data items into one or more clusters, based on the calculated probabilities; and, optionally, sharing clusters and meta-clusters between users.
34 Citations
18 Claims
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1. A computer-implemented method for dynamically clustering data items, the method comprising:
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receiving (a) a plurality of data items originating from at least two sources;
(b) a plurality of distinct metadata details;
(c) data indicative of associations between said data items and said metadata details, wherein each data item is associated with at least one metadata detail, and wherein at least a first data item originating from a first source and a second data item originating from a second source are related data items associated with at least one shared metadata detail;grading strengths of relationships between at least one of said data items and at least one of said metadata details; and clustering said data items into one or more clusters, based on the calculated grades wherein at least one of said clusters comprises related data items originating from more than one source, wherein said grading comprises applying weighting functions and the weighting functions are rule-based weighting functions. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for dynamically clustering data items, the method comprising:
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receiving (a) a plurality of data items originating from at least two sources;
(b) a plurality of distinct metadata details;
(c) data indicative of associations between said data items and said metadata details, wherein each data item is associated with at least one metadata detail, and wherein at least a first data item originating from a first source and a second data item originating from a second source are related data items associated with at least one shared metadata detail;grading strengths of relationships between at least one of said data items and at least one of said metadata details; clustering said data items into one or more clusters, based on the calculated grades wherein at least one of said clusters comprises related data items originating from more than one source; clustering said one or more clusters into meta clusters; and ranking said clusters within said meta-clusters in accordance with the relevance of said clusters. - View Dependent Claims (9)
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10. A system for dynamically clustering data items, the system comprising at least one processing unit configured to:
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receive (a) a plurality of data items originating from at least two sources;
(b) a plurality of distinct metadata details;
(c) data indicative of associations between said data items and said metadata details, wherein each data item is associated with at least one metadata detail, and wherein at least a first data item originating from a first source and a second data item originating from a second source are related data items associated with at least one shared metadata detail;grade strengths of relationships between at least one of said data items and at least one of said metadata details; and cluster said data items into one or more clusters, based on the calculated grade wherein at least one of said clusters comprises related data items originating from more than one source, wherein said grade comprises applying weighting functions, and the weighting functions are rule-based weighting functions. - View Dependent Claims (11, 12, 13, 14, 15, 16)
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17. A system for dynamically clustering data items, the system comprising at least one processing unit configured to:
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receive (a) a plurality of data items originating from at least two sources;
(b) a plurality of distinct metadata details;
(c) data indicative of associations between said data items and said metadata details, wherein each data item is associated with at least one metadata detail, and wherein at least a first data item originating from a first source and a second data item originating from a second source are related data items associated with at least one shared metadata detail;grade strengths of relationships between at least one of said data items and at least one of said metadata details; cluster said data items into one or more clusters, based on the calculated grade wherein at least one of said clusters comprises related data items originating from more than one source; cluster said one or more clusters into meta clusters; and rank said clusters within said meta-clusters in accordance with the relevance of said clusters. - View Dependent Claims (18)
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Specification