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Activity Number:
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335
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Type:
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Invited
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Date/Time:
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Wednesday, August 6, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Memorial
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| Abstract - #300383 |
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Title:
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Multidimensional Scaling for Large Genomic Data Sets
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Author(s):
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Jengnan Tzeng and Henry H.S. Lu*+ and Wen-Hsiung Li
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Companies:
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Academia Sinica and National Chiao Tung University and The University of Chicago
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Address:
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1101 Ta Hsueh Road, Hsinchu, 30050, Taiwan
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Keywords:
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Multi-dimensional scaling (MDS) ; dimension reduction ; clustering ; K-means ; microarray ; cell cycle
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Abstract:
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We developed a new rapid metric MDS method with a low computational complexity, making metric MDS applicable for large data sets. Computer simulation showed that the new method of split-and-combine MDS (SC-MDS) is fast, accurate and efficient. Our empirical studies using microarray data on the yeast cell cycle showed that the performance of K-means in the reduced dimensional space is similar to or slightly better than that of K-means in the original space, but about three times faster to obtain the clustering results. Our clustering results using SC-MDS are more stable than those in the original space. Hence, the proposed SC-MDS is useful for analyzing whole genome data.
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