The Afinity Propagation (AP) clustering algorithm proposed by Frey and Dueck (2007) provides an understandable, nearly optimal summary of a dataset, albeit with quadratic compu- tational complexity. This paper, motivated by Autonomic Computing, extends AP to the data streaming framework. Firstly a hierarchical strategy is used to reduce the complex- ity to O(N 1+ε); the distortion loss incurred is analyzed in relation with the dimension of the data items. Secondly, a coupling with a change detection test is used to cope with non-stationary data distribution, and rebuild the model as needed. The presented approach Strap is applied to the stream of jobs submitted to the EGEE Grid, providing an understandable description of the job ow and enabling the system administrator to spot online some sources of fail- ures. Copyright 2009 ACM.
|Title of host publication
|Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
|Number of pages
|Published - Nov 16 2009