Tracing cluster transitions for different cluster types

Clustering algorithms detect groups of similar pop- ulation members, like customers, news or genes. In many cluster- ing applications the observed population evolves and changes over time, subject to internal and external factors. Detecting and under- standing changes is important for decision support. In this work, we present the MONIC + framework for cluster-type-specific transi- tion modeling and detection. MONIC + encompasses a typification of clusters and cluster-type-specific transition indicators, by exploit- ing cluster topology and cluster statistics for the transition detec- tion process. Our experiments on both synthetic and real datasets demonstrate the usefulness and applicability of our framework. Keywords: dynamic environments, change detection, cluster transitions, transition indicators, cluster-type-specific indicators.

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