Exploratory Multilevel Hot Spot Analysis: Australian Taxation Office Case Study

Denny, Williams, G.J. and Christen, P.

Population based real-life datasets often contain smaller clusters of unusual sub-populations. While these clusters, called 'hot spots', are small and sparse, they are usually of special interest to an analyst. In this paper we introduce a visual drill-down Self-Organizing Map (SOM)-based approach to explore such hot spots characteristics in real-life datasets. Iterative clustering algorithms (such as k-means) and SOM are not designed to show these small and sparse clusters in detail. The feasibility of our approach is demonstrated using a large real life dataset from the Australian Taxation Office.

Cite as: Denny, Williams, G.J. and Christen, P. (2007). Exploratory Multilevel Hot Spot Analysis: Australian Taxation Office Case Study. In Proc. Sixth Australasian Data Mining Conference (AusDM 2007), Gold Coast, Australia. CRPIT, 70. Christen, P., Kennedy, P. J., Li, J., Kolyshkina, I. and Williams, G. J., Eds. ACS. 77-84.

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