Abstract #300917


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JSM 2002 Abstract #300917
Activity Number: 211
Type: Contributed
Date/Time: Tuesday, August 13, 2002 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing*
Abstract - #300917
Title: Efficient Clustering Algorithms Via Multivariate Techniques and Mixture Models
Author(s): Yun-Fei Chen*+
Affiliation(s): Eli Lilly and Company
Address: Lilly Corporate Center, Indianapolis, Indiana, 46285, USA
Keywords: clustering ; principal component ; binary decision tree ; mixture model
Abstract:

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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Revised March 2002