Research of parallel DBSCAN clustering algorithm based on MapReduce
Xiufen Fu, Shanshan Hu, Yaguang Wang · International Journal of Database Theory and Application · 2014
For the lack of density-based spatial clustering with noise (DBSCAN) algorithm in dealing with large data sets, MapReduce programming model is proposed to achieve the clustering of DBSCAN. Map functions to complete the data analysis, and get clustering rules in different data objects; Then Reduce functions merge these clustering rules to get a final result. Experimental results show: the DBSCAN of MapReduce running on the cloud computing platform Hadoop has good speedup and scalability.