Distributed data stream clustering algorithm based on affinity propagation

Abstract

As to the low clustering quality and high communication cost of the existed distributed clustering algorithm, a distributed data stream clustering algorithm( DAPDC) which combined the density with the idea of representative points clustering was proposed. The concept of the class cluster representative point to describe the local distribution of data flows was introduced in the local sites using affinity propagation clustering, while the global site got the global model by merging the summary data structure that was uploaded from the local site by the improved density clustering algorithm. The simulation results show that DAPDC can improve the clustering quality of data streams in distributed environment significantly.Simultaneously, the algorithm can find the clusters of different shapes and reduce the amount of data transferred significantly by using class cluster representative points.