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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks, Volume 16
Editor
Cihan H. Dagli
Cihan H. Dagli
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Anna L. Buczak
Anna L. Buczak
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David L. Enke
David L. Enke
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Mark Embrechts
Mark Embrechts
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Okan Ersoy
Okan Ersoy
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ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006

In this paper, we reintroduce Hierarchical Mode Analysis (HMA), which was first proposed in 1968, as a powerful clustering algorithm for bioinformatics. The ability of HMA to find a compact hierarchy of a small number of dense clusters is very important in many bioinformatics problems (for example, when clustering genes in a set of gene-expression microarrays, where only a small number of genes related to the experimental context cluster well, while the rest need to be pruned). We also present two major improvements on HMA: a faster approximation algorithm, and a novel 2-D visualization scheme for high-dimensional datasets. These two improvements make HMA a powerful and promising new tool for many large, high-dimensional clustering problems in bioinformatics. We present empirical results on the Gasch dataset showing the effectiveness of our framework.

Abstract
Introduction
Speeding up HMA
Visualizing HMA
Experimental Evaluation
References
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