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Activity Number: 215
Type: Contributed
Date/Time: Monday, August 3, 2009 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #304717
Title: Clustering High-Dimension, Low Sample--Size Data
Author(s): Jeongyoun Ahn*+ and Youngjoo Yoon
Companies: The University of Georgia and The University of Georgia
Address: 101 Cedar St., Athens, GA, 30602,
Keywords: High dimension low sample size ; cluster analysis ; maximal data piling
Abstract:

In clustering with high dimension, low sample size data, traditional distance measures between clusters, such as complete linkage and single linkage, etc., are no longer valid. The maximal data piling distance, which can be obtained from a binary classification problem, is a distance between hyperplanes of two classes and is a natural distance measure between HDLSS clusters. In this talk, we introduce a hierarchical clustering method based on the maximal data piling distance. Empirical and theoretical properties of the method will be presented.

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