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Abstract Details
Activity Number:
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176
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #305882 |
Title:
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Consistent Biclustering
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Author(s):
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Cheryl J. Flynn*+ and Patrick O Perry
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Companies:
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New York University and NYU Stern
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Address:
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329 E 88th St, New York, NY, 10128, United States
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Keywords:
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Biclustering ;
Consistency ;
Microarray Data ;
Directed Networks ;
Term-Document Matrices
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Abstract:
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Biclustering, the process of simultaneously clustering observations and variables, is a popular and effective tool for finding structure in a high-dimensional dataset. A variety of biclustering algorithms exist, and they have been applied successfully to data sources ranging from gene expression arrays to term-document matrices. Currently, while biclustering appears to work well in practice, there have been no theoretical guarantees about its performance. We address this shortcoming with a theorem providing sufficient conditions for asymptotic consistency when both the number of observations and the number of variables in the dataset tend to infinity. This theorem applies to a broad range of data distributions, including Gaussian, Poisson, and Bernoulli. We demonstrate our results through a simulation study and with examples drawn from microarray analysis, document clustering, and network analysis.
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