Tracing cluster transitions for different cluster types

• Autorzy: Ntoutsi I. , Spiliopoulou M. , Theodoridis Y.
• Języki publikacji: EN

Abstrakty

EN

Clustering algorithms detect groups of similar population members, like customers, news or genes. In many clustering applications the observed population evolves and changes over time, subject to internal and external factors. Detecting and understanding changes is important for decision support. In this work, we present the MONIC+ framework for cluster-type-specific transition modeling and detection. MONIC+ encompasses a typification of clusters and cluster-type-specific transition indicators, by exploiting cluster topology and cluster statistics for the transition detection process. Our experiments on both synthetic and real datasets demonstrate the usefulness and applicability of our framework.

Słowa kluczowe

EN

dynamic environments; change detection; cluster-type-specific indicators

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2009
• Tom: Vol. 38, no 1
• Strony: 239--259
• Opis fizyczny: Bibliogr. 20 poz., rys.

Twórcy

autor

Ntoutsi I.

autor

Spiliopoulou M.

autor

Theodoridis Y.

• Department of Informatics, University of Piraeus, Greece,

Bibliografia

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