DATA CLUSTERING SYSTEM AND METHOD
First Claim
1. A non-transitory computer-readable medium storing program code, the program code executable by a processor of a computing system to cause the computing system to:
- identify a first dataset comprising n data samples;
identify b data samples of the n data samples of the first dataset, wherein b is less than n;
create a first plurality of datasets, each of the first plurality of datasets comprising m data samples, where m is greater than b, and wherein each of the m data samples of each of the first plurality of datasets is selected from the b data samples;
identify c data samples of the n data samples of the first dataset, wherein c is less than n, and wherein the c data samples are not identical to the b data samples;
create a second plurality of datasets, each of the second plurality of datasets comprising p data samples, where p is greater than c, and wherein each of the p data samples of each of the second plurality of datasets is selected from the c data samples;
for each of the b data samples, identify a cluster based on the first plurality of datasets; and
for each of the c data samples, identify a cluster based on the second plurality of datasets.
1 Assignment
0 Petitions
Accused Products
Abstract
A system includes identification of a first dataset comprising n data samples, identification of b data samples of the n data samples of the first dataset, wherein b is less than n, creation of a first plurality of datasets, each of the first plurality of datasets comprising m data samples, where m is greater than b, and wherein each of the m data samples of each of the first plurality of datasets is selected from the b data samples, identification of c data samples of the n data samples of the first dataset, wherein c is less than n, and wherein the c data samples are not identical to the b data samples, creation of a second plurality of datasets, each of the second plurality of datasets comprising p data samples, where p is greater than c, and wherein each of the p data samples of each of the second plurality of datasets is selected from the c data samples, identification, for each of the b data samples, of a cluster based on the first plurality of datasets, and identification, for each of the c data samples, of a cluster based on the second plurality of datasets.
19 Citations
18 Claims
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1. A non-transitory computer-readable medium storing program code, the program code executable by a processor of a computing system to cause the computing system to:
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identify a first dataset comprising n data samples; identify b data samples of the n data samples of the first dataset, wherein b is less than n; create a first plurality of datasets, each of the first plurality of datasets comprising m data samples, where m is greater than b, and wherein each of the m data samples of each of the first plurality of datasets is selected from the b data samples; identify c data samples of the n data samples of the first dataset, wherein c is less than n, and wherein the c data samples are not identical to the b data samples; create a second plurality of datasets, each of the second plurality of datasets comprising p data samples, where p is greater than c, and wherein each of the p data samples of each of the second plurality of datasets is selected from the c data samples; for each of the b data samples, identify a cluster based on the first plurality of datasets; and for each of the c data samples, identify a cluster based on the second plurality of datasets. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computing system comprising:
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a memory storing processor-executable program code; and a processor to execute the processor-executable program code in order to cause the computing system to; identify a first dataset comprising n data samples; identify b data samples of the n data samples of the first dataset, wherein b is less than n; create a first plurality of datasets, each of the first plurality of datasets comprising m data samples, where m is greater than b, and wherein each of the m data samples of each of the first plurality of datasets is selected from the b data samples; identify c data samples of the n data samples of the first dataset, wherein c is less than n, and wherein the c data samples are not identical to the b data samples; create a second plurality of datasets, each of the second plurality of datasets comprising p data samples, where p is greater than c, and wherein each of the p data samples of each of the second plurality of datasets is selected from the c data samples; for each of the b data samples, identify a cluster based on the first plurality of datasets; and for each of the c data samples, identify a cluster based on the second plurality of datasets. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A computer-implemented method, comprising:
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identifying a first dataset comprising n data samples; identifying b data samples of the n data samples of the first dataset, wherein b is less than n; creating a first plurality of datasets, each of the first plurality of datasets comprising m data samples, where m is greater than b, and wherein each of the m data samples of each of the first plurality of datasets is selected from the b data samples; identifying c data samples of the n data samples of the first dataset, wherein c is less than n, and wherein the c data samples are not identical to the b data samples; creating a second plurality of datasets, each of the second plurality of datasets comprising p data samples, where p is greater than c, and wherein each of the p data samples of each of the second plurality of datasets is selected from the c data samples; for each of the b data samples, identifying a cluster based on the first plurality of datasets; and for each of the c data samples, identifying a cluster based on the second plurality of datasets. - View Dependent Claims (14, 15, 16, 17, 18)
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Specification