Exploratory multilevel hot spot analysis: Australian taxation office case study

Authors:
Denny;Graham J. Williams;Peter Christen
Affiliations:
The Australian National University, Canberra, Australia and University of Indonesia;The Australian Taxation Office and The Australian National University, Canberra, Australia;The Australian National University, Canberra, Australia
Venue:
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Year:
2007

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Abstract

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.