METHODS AND APPARATUS FOR PERFORMING TRANSFORMATION TECHNIQUES FOR DATA CLUSTERING AND/OR CLASSIFICATION
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Abstract
Some aspects include transforming data for which at least one constraint has been specified on a portion of the data, the at least one constraint relating to a similarity and/or dissimilarity of at least some of the portion of the data. Techniques comprise determining a first transformation that approximates the at least one constraint using a cosine similarity as a measure of the similarity and/or dissimilarity of the at least a portion of the data, applying at least the first transformation to the data to obtain transformed data, and fitting a plurality of clusters to the transformed data to obtain a plurality of established clusters. Some aspects include classifying input data by transforming the input data using at least the first transformation and comparing the transformed input data to the established clusters.
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Citations
66 Claims
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1-30. -30. (canceled)
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31. A method of classifying input data as belonging to one of a plurality of classifications, the plurality of classifications associated with a respective plurality of clusters that were fit to training data, the method comprising:
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obtaining a first transformation used to transform the training data when the plurality of clusters were fit to the training data, the first transformation approximating at least one constraint relating to a similarity and/or dissimilarity of at least a portion of the training data, wherein the first transformation was determined using a cosine similarity as a measure of the similarity and/or dissimilarity of the at least a portion of the training data; transforming the input data using at least the first transformation to obtain transformed input data; comparing the transformed input data to the plurality of clusters to determine which cluster of the plurality of clusters the input data should be associated with; and classifying the input data according to a classification of the plurality of classifications associated with the cluster that the input data was determined to be associated with. - View Dependent Claims (32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42)
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43. At least one computer readable storage medium storing instructions, that when executed by at least one processor, perform a method of classifying input data as belonging to one of a plurality of classifications, the plurality of classifications associated with a respective plurality of clusters that were fit to training data, the method comprising:
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obtaining a first transformation used to transform the training data when the plurality of clusters were fit to the training data, the first transformation approximating at least one constraint relating to a similarity and/or dissimilarity of at least a portion of the training data, wherein the first transformation was determined using a cosine similarity as a measure of the similarity and/or dissimilarity of the at least a portion of the training data; transforming the input data using at least the first transformation to obtain transformed input data; comparing the transformed input data to the plurality of clusters to determine which cluster of the plurality of clusters the input data should be associated with; and classifying the input data according to a classification of the plurality of classifications associated with the cluster that the input data was determined to be associated with. - View Dependent Claims (44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54)
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55. A system for classifying input data as belonging to one of a plurality of classifications, the plurality of classifications associated with a respective plurality of clusters that were fit to training data, the system comprising:
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at least one computer readable storage medium for storing the input data and for storing a first transformation used to transform the training data when the plurality of clusters were fit to the training data, the first transformation approximating at least one constraint relating to a similarity and/or dissimilarity of at least a portion of the training data, wherein the first transformation was determined using a cosine similarity as a measure of the similarity and/or dissimilarity of the at least a portion of the training data; and at least one processor capable of accessing the at least one computer readable storage medium, the at least one processor configured to; transform the input data using at least the first transformation to obtain transformed input data; compare the transformed input data to the plurality of clusters to determine which cluster of the plurality of clusters the input data should be associated with; and classify the input data according to a classification of the plurality of classifications associated with the cluster that the input data was determined to be associated with. - View Dependent Claims (56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66)
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Specification