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Activity Number:
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202
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Type:
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Contributed
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Date/Time:
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Monday, August 7, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #307584 |
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Title:
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Mixture Gaussian Model-Based Bayesian Clustering
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Author(s):
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Wei Zhang*+
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Companies:
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Harvard University
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Address:
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1 Oxford Street, Cambridge, MA, 02138,
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Keywords:
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mixture Gaussian model ; Bayesian clustering ; generalized Gibbs sampler
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Abstract:
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Identifying patterns of co-expression in DNA microarray by cluster analysis is an important task in understanding the underlying biological processes. In this presentation, we will introduce a mixture Gaussian model-based Bayesian clustering method. The model gives freedoms of drift and scale effects and allows different S/N for different clusters. Due to the high dependency structure between the parameters, some advanced techniques of the gibbs sampler were used to decrease the autocorrelation of the sampled parameters. Some simulation studies show that this model-based Bayesian clustering method is more robust and flexible than the traditional k-means clustering.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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