Abstract
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.
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Denny, Williams, G.J., Christen, P. (2008). Exploratory Hot Spot Profile Analysis Using Interactive Visual Drill-Down Self-Organizing Maps. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_48
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DOI: https://doi.org/10.1007/978-3-540-68125-0_48
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