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Activity Number:
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211
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Type:
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Contributed
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Date/Time:
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Tuesday, August 13, 2002 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing*
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| Abstract - #300917 |
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Title:
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Efficient Clustering Algorithms Via Multivariate Techniques and Mixture Models
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Author(s):
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Yun-Fei Chen*+
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Affiliation(s):
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Eli Lilly and Company
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Address:
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Lilly Corporate Center, Indianapolis, Indiana, 46285, USA
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Keywords:
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clustering ; principal component ; binary decision tree ; mixture model
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Abstract:
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We develop new efficient clustering algorithms based on multivariate techniques and Gaussian mixtures. For two-class and three-class clustering, we first cluster the data points along the first principal component and then apply linear discriminant analysis to refine the clustering. To extend this strategy to multi-class clustering, a binary decision tree approach and an agglomerative linkage technique are employed. Furthermore, when we use a mixture model on the clustered data, more accurate clustering can be obtained. The EM (expectation-maximization) algorithm is adopted for parameter estimation. Simulations show that our algorithms can achieve more accurate clustering and have much faster speed than existing methods.
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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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