JSM 2004 Online Program

Abstract #300057

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Activity Number:	32
Type:	Invited
Date/Time:	Sunday, August 8, 2004 : 4:00 PM to 5:50 PM
Sponsor:	Statistical and Applied Mathematical Sciences Institute
Abstract - #300057
Title:	Scalability of Models in Data Mining
Author(s):	David Banks*+
Companies:	Duke University
Address:	ISDS Box 90251, Durham, NC, 27708,
Keywords:	Curse of Dimensionality ; large p, small n ; clustering ; classification ; data mining ; selection
Abstract:	The SAMSI data mining year examined a range of problems in the area, from use of unlabeled sample in classification to issues in overcompleteness. A major theme concerned the analysis of high-dimension, low sample-size datasets. This talk reviews various strategies for handling these problems, which are closely related to the Curse of Dimensionality and also relevant to data quality. Our work explores the effect of combining smart algorithms, aggressive feature selection, and combinatorial search. The ideas are illustrated through several examples, and we draw conclusions and give advice for those who must analyze such data.

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