Diffused and emerging incremental clustering algorithm
Shen Nan · Jisuanji gongcheng yu sheji · 2012
In order to effectively cluster dynamic data,properly handle the relationship between the new data and the existing class,and improve clustering efficiency and utilization of computing resources,a diffused and emerging incremental clustering algorithm(DEICA) is proposed.On the basis of diffused and emerging clustering algorithm(DECA),affinity propagation(AP) is used to improve division mechanism of the algorithm,so old and new data are efficiently clustered.Many experiments demonstrate our algorithm can achieve clustering of dynamic data and improve the efficiency of dynamic data aggregation and the utilization of resource.