Self-organizing maps-based temporal cluster analysis
Discovering clustering changes in real-life datasets is important in many contexts, such as fraud detection and customer attrition analysis. Organizations can use such knowledge of change to adapt business strategies in response to changing circumstances. To understand what has changed, analysts have to be able to relate new knowledge acquired from a newer dataset to that acquired from an earlier dataset. This PhD thesis presents a comprehensive visual-interactive temporal clustering analysis...[Show more]
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