Using affinity measures with supervised classifiers
First Claim
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1. A method comprising:
- developing a supervised classifier; and
determining a non-binary affinity measure between two data points using said supervised classifier.
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Abstract
A non-binary affinity measure between any two data points for a supervised classifier may be determined. For example, affinity measures may be determined for tree, kernel-based, nearest neighbor-based and neural network supervised classifiers. By providing non-binary affinity measures using supervised classifiers, more information may be provided for clustering, analyzing and, particularly, for visualizing the results of data mining.
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Citations
30 Claims
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1. A method comprising:
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developing a supervised classifier; and
determining a non-binary affinity measure between two data points using said supervised classifier. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. An article comprising a medium storing instructions that, if executed, enable a processor-based system to:
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develop a supervised classifier; and
determine a non-binary affinity measure between data points using said supervised classifier. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A system comprising:
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a processor; and
a storage coupled to said processor, the storage storing instructions to develop a supervised classifier and determine a non-binary affinity measure between two data points using the supervised classifier. - View Dependent Claims (26, 27, 28, 29, 30)
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Specification