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Density-based cluster mining is known to serve a broad range of applications ranging from stock trade analysis to moving object monitoring. Although methods for efficient extraction of density-based clusters have been studied in the literature, the problem of summarizing and matching of such clusters with arbitrary shapes and complex cluster structures remains unsolved. Therefore, the goal of the authors' work is to extend the state-of-art of density-based cluster mining in streams from cluster extraction only to now also support analysis and management of the extracted clusters. Their work solves three major technical challenges.
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