| Autor: |
Denny, Williams, Graham J., Christen, Peter |
| Zdroj: |
Advances in Knowledge Discovery & Data Mining: 12th Pacific-Asia Conference, Pakdd 2008 Osaka, Japan, May 20-23, 2008 Proceedings; 2008, p536-543, 8p |
| Abstrakt: |
Real-life datasets often contain small clusters of unusual sub-populations. These clusters, or `hot spots΄, are usually sparse and of special interest to an analyst. We present a methodology for identifying hot spots and ranking attributes that distinguish them interactively, using visual drill-down Self-Organizing Maps. The methodology is particularly useful for understanding hot spots in high dimensional datasets. Our approach is demonstrated using a large real life taxation dataset. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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