Research of efficiency of partitioning clustering algorithm based on graphics processing unit
基于图形处理器的划分聚类算法效率研究*

This paper proposed a mode with CPU+GPU co-processing to improve the efficiency of enhanced_K-means algorithm. By the characterization that the parallel computing could be finished by the multiple fragment processor, the step that the calculation and comparison of Euclidean distance, the judgment on the point aggregation in the clustering that has no difference after the central point was changed, both of which would spent much time, were finished by GPU, while other steps were finished by CPU. Therefore the clustering efficiency was improved greatly. Some experiments conducted in a PC with Pentium 4 3.4GHz AMD 643500+ CPU and NVIDIA GeForce7800GT graphic card demonstrate that the presented algorithm is faster than the previous CPU-based algorithms, thus the improved partitional clustering algorithm is applicable for the clustering data stream that requiring for high speed processing and high quality clustering results.