Data stream clustering algorithm based on probability density
一种基于概率密度的数据流聚类算法

Data stream is characterized by infinite data and quick stream speed, so traditional clustering algorithm cannot be applied to data stream clustering directly, In view of above questions, a probability-density-based data stream clustering algorithm was proposed. It requires only newly arrived data, not the entire historical data, to be saved in memory. It applies EM algorithm on the newly arrived data and updates probability-density function by incremental Gaussian mixture model. Experimental results show that the algorithm is very effective to solve data stream clustering.