Tracing cluster transitions for different cluster types

Clustering algorithms detect groups of similar population members, like customers, news or genes. In many clustering applications the observed population evolves and changes, subject to internal and external factors. Detecting and understanding change is important for decision support. We extend our earlier framework MONIC for cluster transition modeling and detection, into MONIC for cluster-type specific transition monitoring. MONIC encompasses a typification of clusters and cluster-type-specific transition indicators, by exploiting cluster topology and cluster statistics for transition detection. 3