As a typical representative of clustering algorithm

DBSCAN algorithm has the advantages of fast speed and helps to find the noise of data. However

in big data processing

there are problems of low clustering efficiency

high memory and I/O requirement

and poor clustering precision. With the support of cluster computer technology especially the development of cloud computing

the solutions to the problems of DBSCAN algorithm mentioned above can be provided and progressed significantly. This paper proposes a parallel PMDBSCAN algorithm based on data partition which can pre-process data partition before clustering

realize parallelization of DBSCAN algorithm by parallel programming model MapReduce

and reduce I/O consumption according to overlapping partition. The results show that in dealing with large-scale data the PMDBSCAN algorithm increases the speed of clustering

reduces I/O consumption and improves cluster quality significantly.