Representative Based Data Stream Clustering algorithm

To solve the problem of data streams clustering, the algorithm RB-Stream (Representative-Based Data Stream Clustering) was proposed. Firstly, this paper presented the concept of circular-point based on the representative points and designed the iterative algorithm to find the density-connected circular-points representing the clusters. Secondly, the author designed the adjacent list to save clusters for both storage and retrieve efficiency. The RB-Stream algorithm can find the clusters of different shapes under the data stream environment, it is capable of capturing the evolving clusters by introducing the temporal density. The experiments show that the RB-Stream algorithm is feasible and scale expandable.