@inproceedings{fce8e646b82544e2ba9965ceadc2df37,
title = "Exploratory multilevel hot spot analysis: Australian taxation office case study",
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.",
keywords = "Cluster analysis, Drill-down, Imbalanced data, Neural network, Self-organizing maps, Visualization",
author = "Denny and Williams, {Graham J.} and Peter Christen",
year = "2007",
month = jan,
day = "1",
language = "English",
isbn = "9781920682514",
series = "Conferences in Research and Practice in Information Technology Series",
publisher = "Australian Computer Society",
pages = "77--84",
booktitle = "Data Mining and Analytics 2007 - 6th Australasian Data Mining Conference, AusDM 2007, Proceedings",
address = "Australia",
note = "6th Australasian Data Mining Conference, AusDM 2007 ; Conference date: 03-12-2007 Through 04-12-2007",
}