- 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.
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