Type
ArticleKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2013-08-23Online Publication Date
2013-08-23Print Publication Date
2014-07Permanent link to this record
http://hdl.handle.net/10754/556655
Metadata
Show full item recordAbstract
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.Citation
Data Stream Clustering With Affinity Propagation 2014, 26 (7):1644 IEEE Transactions on Knowledge and Data Engineeringae974a485f413a2113503eed53cd6c53
10.1109/TKDE.2013.146