Exploratory multilevel hot spot analysis: Australian taxation office case study

Denny, Graham J. Williams, Peter Christen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationData Mining and Analytics 2007 - 6th Australasian Data Mining Conference, AusDM 2007, Proceedings
PublisherAustralian Computer Society
Pages77-84
Number of pages8
ISBN (Print)9781920682514
Publication statusPublished - 1 Jan 2007
Event6th Australasian Data Mining Conference, AusDM 2007 - Gold Coast, QLD, Australia
Duration: 3 Dec 20074 Dec 2007

Publication series

NameConferences in Research and Practice in Information Technology Series
Volume70
ISSN (Print)1445-1336

Conference

Conference6th Australasian Data Mining Conference, AusDM 2007
CountryAustralia
CityGold Coast, QLD
Period3/12/074/12/07

Keywords

  • Cluster analysis
  • Drill-down
  • Imbalanced data
  • Neural network
  • Self-organizing maps
  • Visualization

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