Machine Learning and Knowledge Discovery in Databases

Volume 5212 of the series Lecture Notes in Computer Science pp 628-643

Data Streaming with Affinity Propagation

  • Xiangliang ZhangAffiliated withTAO − INRIA CNRS
  • , Cyril FurtlehnerAffiliated withTAO − INRIA CNRS
  • , Michèle SebagAffiliated withTAO − INRIA CNRS

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This paper proposed StrAP (Streaming AP), extending Affinity Propagation (AP) to data steaming. AP, a new clustering algorithm, extracts the data items, or exemplars, that best represent the dataset using a message passing method. Several steps are made to build StrAP. The first one (Weighted AP) extends AP to weighted items with no loss of generality. The second one (Hierarchical WAP) is concerned with reducing the quadratic AP complexity, by applying AP on data subsets and further applying Weighted AP on the exemplars extracted from all subsets. Finally StrAP extends Hierarchical WAP to deal with changes in the data distribution. Experiments on artificial datasets, on the Intrusion Detection benchmark (KDD99) and on a real-world problem, clustering the stream of jobs submitted to the EGEE grid system, provide a comparative validation of the approach.