Parallel Optimization K-means Algorithm Facing the Data Size Scalable
Traditional K-means algorithm need to load all the sample data into memory, and updating the class center is a non-parallel process. For the problem of the number of processing data is small and updating class centers with low speed in traditional K-means algorithm, pro-poses an improved K-means algorithm to solve the problems of processing data scale expansion and the processor utilization inefficient. Experiment shows the method can efficiently deal with large-scale data clustering.
NETL
NSTL
万方数据
维普
