基于差异性采样的流数据聚类算法

oncerning the problems of high time complexity, large storage space requirements and low accuracy when traditional clustering algorithm cluster stream data, this paper proposed a kind of stream data clustering algorithm based on differential sampling. First, it used the differential sampling method sampled stream data, and used sample points to construct kernel matrix. Then it used kernel fuzzy C-means clustering algorithm clustered the data points in the kernel matrix, obtained a marked sample kernel matrix. Finally, using the marked kernel matrix divided the stream data. Meanwhile, this paper adopted the fading cluster mechanism to update kernel matrix in real time. Experimental results show that compared with the traditional clustering algorithm, the proposed algorithm achieves lower time complexity, real-time clustering at the same time, get the ideal clustering result.