Data Mining Methods for Clustering Large Two-way Data to Identify Local Structures and Global Patterns
*Minho Chae, FDA Chun-houh Chen, Institute of Statistical Science, Academica Sinica Wen Zou, FDA James J Chen, FDA Keywords: Clustering, Visualization, RSVD In microarray data hierarchical clustering (HC) is often used to group objects (samples) according to observations (gene expression profiles) to discern possible patterns in the data. While HC can quickly reveal local behavior of the data, it hardly shows global patterns and smooth transitional trends. Our approach overcomes this shotcoming of HC by employing robust singular value decomposition (RSVD) in order to smoothly sort internal nodes of HC since RSVD can reveal bilinear structure of the data with missing values and outliers. In addition to a microarray data, this approach also applied to Adverse Event Reporting System’s (AERS) data of FDA and presented with intuitive matrix visualization such as generalized association plot (GAP).
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Key Dates
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April 30 - May 22, 2013
Invited Abstract Submission Open -
June 4, 2013
Online Registration Opens -
August 9 - August 23, 2013
Invited Abstract Editing -
August 23, 2013
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August 26, 2013
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September 9, 2013
Cancellation Deadline and Registration Closes @ 11:59 pm EDT -
September 16 - September 18, 2013
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