K-means Clustering Optimization Algorithm Based on MapReduce

Aiming at the defects of traditional K-means clustering algorithm for big data, this paper provides K-means clustering mining optimization algorithm based on big data, shows a MapReduce software architecture which is suitable for large data processing mechanism, provides an improved method for selecting initial clustering centers and puts forward a K-means algorithm optimization based on MapReduce model. The improved algorithm is applied to the coal quality analysis, the result shows that compared with traditional algorithms, the optimization algorithm improves the efficiency of the algorithm obviously, and the accuracy is also enhanced.