Data Stream Clustering With Affinity Propagation

Xiangliang Zhang, Cyril Furtlehner, Cecile Germain-Renaud, Michele Sebag

Research output: Contribution to journalArticlepeer-review

59 Scopus citations

Abstract

Data stream clustering provides insights into the underlying patterns of data flows. This paper focuses on selecting the best representatives from clusters of streaming data. There are two main challenges: how to cluster with the best representatives and how to handle the evolving patterns that are important characteristics of streaming data with dynamic distributions. We employ the Affinity Propagation (AP) algorithm presented in 2007 by Frey and Dueck for the first challenge, as it offers good guarantees of clustering optimality for selecting exemplars. The second challenging problem is solved by change detection. The presented StrAP algorithm combines AP with a statistical change point detection test; the clustering model is rebuilt whenever the test detects a change in the underlying data distribution. Besides the validation on two benchmark data sets, the presented algorithm is validated on a real-world application, monitoring the data flow of jobs submitted to the EGEE grid.
Original languageEnglish (US)
Pages (from-to)1644-1656
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume26
Issue number7
DOIs
StatePublished - Aug 23 2013

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