Denny, Denny; Williams, Graham; Christen, Peter
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...[Show more]
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