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Published in:
2013 | OriginalPaper | Chapter
MONIC and Followups on Modeling and Monitoring Cluster Transitions
Authors : Myra Spiliopoulou, Eirini Ntoutsi, Yannis Theodoridis, Rene Schult
Published in: Machine Learning and Knowledge Discovery in Databases
Publisher: Springer Berlin Heidelberg
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There is much recent discussion on data streams and big data, which except of their volume and velocity are also characterized by volatility. Next to detecting change, it is also important to interpret it. Consider customer profiling as an example: Is a cluster corresponding to a group of customers simply disappearing or are its members migrating to other clusters? Does a new cluster reflect a new type of customers or does it rather consist of old customers whose preferences shift? To answer such questions, we have proposed the framework MONIC [20] for modeling and tracking cluster transitions. MONIC has been re-discovered some years after publication and is enjoying a large citation record from papers on community evolution, cluster evolution, change prediction and topic evolution.