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Activity Number:
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436
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #305303 |
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Title:
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Clustering Relationships in High-Dimensional Data
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Author(s):
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Timothy Au*+ and Wei Jiang
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Companies:
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Cornell University and Hong Kong University of Science and Technology
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Address:
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, , ,
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Keywords:
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Clustering ; Profiles
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Abstract:
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In this paper, we propose a model-based clustering technique for describing different relationships among high-dimensional data. The relationship clustering is different from traditional clustering methods such as k-means which use certain distance metrics to group data points. The new method deploys a relationship model (linear) with various distribution assumptions to group data points with similar relationships among the high-dimension attributes. A simple example is shown to illustrate the effectiveness of the proposed method.
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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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