Gaussian mixture models in a data mining system
First Claim
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1. A method for creating analyzing data in a computer-implemented data mining system, comprising:
- (a) accessing data from a database in the computer-implemented data mining system; and
(b) performing an Expectation-Maximization (EM) algorithm in the computer-implemented data mining system to create the Gaussian Mixture Model for the accessed data, wherein the EM algorithm generates an output that describes clustering in the data by computing a mixture of probability distributions fitted to the accessed data.
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Abstract
A computer-implemented data mining system that analyzes data using Gaussian Mixture Models. The data is accessed from a database, and then an Expectation-Maximization (EM) algorithm is performed in the computer-implemented data mining system to create the Gaussian Mixture Model for the accessed data. The EM algorithm generates an output that describes clustering in the data by computing a mixture of probability distributions fitted to the accessed data.
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57 Claims
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1. A method for creating analyzing data in a computer-implemented data mining system, comprising:
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(a) accessing data from a database in the computer-implemented data mining system; and
(b) performing an Expectation-Maximization (EM) algorithm in the computer-implemented data mining system to create the Gaussian Mixture Model for the accessed data, wherein the EM algorithm generates an output that describes clustering in the data by computing a mixture of probability distributions fitted to the accessed data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A computer-implemented data mining system for analyzing data, comprising:
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(a) a computer;
(b) logic, performed by the computer, for;
(1) accessing data stored in a database; and
(2) performing an Expectation-Maximization (EM) algorithm to create the Gaussian Mixture Model for the accessed data, wherein the EM algorithm generates an output that describes clustering in the data by computing a mixture of probability distributions fitted to the accessed data. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57)
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39. An article of manufacture embodying logic for analyzing data in a computer-implemented data mining system, the logic comprising:
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(a) accessing data from a database in the computer-implemented data mining system; and
(b) performing an Expectation-Maximization (EM) algorithm in the computer-implemented data mining system to create the Gaussian Mixture Model for the accessed data, wherein the EM algorithm generates an output that describes clustering in the data by computing a mixture of probability distributions fitted to the accessed data.
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Specification