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Activity Number:
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305
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 5, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statisticians in Defense and National Security
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| Abstract - #301767 |
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Title:
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Prospective Clustering Analysis of Spatial-Temporal Data
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Author(s):
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Daniel Zeng*+
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Companies:
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The University of Arizona
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Address:
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MIS Dept, McClelland Hall 430K, Tucson, AZ, 85721,
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Keywords:
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spatial-temporal data analysis ; supper vector machines
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Abstract:
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This talk focuses on a new spatio-temporal data analysis approach aimed at discovering abnormal spatio-temporal clustering patterns. We propose a quantitative evaluation framework and compare our approach against a widely-used space-time scan statistic-based method under this framework. Our approach is based on a robust clustering engine using support vector machines and incorporates ideas from existing online surveillance methods to track incremental changes over time. Initial experimental results using both simulated and real-world data sets indicate that our approach is able to detect abnormal areas with irregular shapes more accurately than the space-time scan statistic-based method.
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