Document clustering
First Claim
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1. A system for clustering document collection observations, the system comprising:
- a processor; and
a processor-readable storage medium in communication with the processor, wherein the processor-readable storage medium contains one or more programming instructions for;
receiving a plurality of parameter vectors,determining a distribution,receiving a plurality of observations, wherein each observation corresponds to a document collection,selecting an optimal partitioning of the observations based on the distribution, the parameter vectors, and a likelihood function, anddetermining a cluster of observations based on the optimal partitioning.
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Abstract
Methods and systems for clustering document collections are disclosed. A system for clustering observations may include a processor and a processor-readable storage medium. The processor-readable storage medium may contain one or more programming instructions for performing a method of clustering observations. A plurality of parameter vectors and a plurality of observations may be received. A distribution may also be determined. An optimal partitioning of the observations may then be selected based on the distribution, the parameter vectors and a likelihood function.
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Citations
18 Claims
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1. A system for clustering document collection observations, the system comprising:
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a processor; and a processor-readable storage medium in communication with the processor, wherein the processor-readable storage medium contains one or more programming instructions for; receiving a plurality of parameter vectors, determining a distribution, receiving a plurality of observations, wherein each observation corresponds to a document collection, selecting an optimal partitioning of the observations based on the distribution, the parameter vectors, and a likelihood function, and determining a cluster of observations based on the optimal partitioning. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method of clustering observations, the method comprising:
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receiving a plurality of parameter vectors; determining a distribution; receiving a plurality of observations, wherein each observation corresponds to a customer service log, wherein the plurality of observations are time-ordered; selecting, by a processor, an optimal partitioning of the observations based on the distribution, the parameter vectors, and a likelihood function; and determining a cluster of observations based on the optimal partitioning. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17, 18)
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Specification