Abstract #301855

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JSM 2002 Abstract #301855
Activity Number: 321
Type: Contributed
Date/Time: Wednesday, August 14, 2002 : 12:00 PM to 1:50 PM
Sponsor: Section on Statistical Computing*
Abstract - #301855
Title: Adaptive Single Linkage Clustering
Author(s): Derek Stanford*+ and Steven McKinney
Affiliation(s): Insightful Corporation
Address: 1700 Westlake Ave N #500, Seattle, Washington, 98109,
Keywords: cluster ; microarray ; nonparametric ; datamining ; tree ; scalable
Abstract:

We present Adaptive Single Linkage (ASL) clustering, a hierarchical nonparametric clustering method which makes no assumptions about the shape, convexity, or relative size of clusters. ASL utilizes near-neighbor information (such as the minimum spanning tree) to explore the modal structure of spatial point process data. In contrast to many standard clustering methods, ASL is robust to noise and scalable for large datasets. We illustrate our methods using both simulated data and real gene expression microarray data; we also present comparisons to other nonparametric clustering methods and to model-based clustering.

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