MR-SNN: Design of parallel Shared Nearest Neighbor clustering algorithm using MapReduce

Shared Nearest Neighbor (SNN) Clustering is a well-established density based clustering algorithm, which can find clusters of different sizes, shapes, and densities. SNN has been widely adopted in numerous applications. As the size of dataset becomes extremely large nowadays, it is inefficient or even impossible for large-scale data to be stored and processed on a single machine. Therefore, the scalability problem of clustering algorithm running on a single machine has to be addressed. In this paper, we improve the traditional SNN clustering algorithm by utilizing high-performance computing clusters and powerful programming platform (MapReduce) for big data analysis. In particular, we design the MapReduce-based Shared Nearest Neighbor clustering algorithm called MR-SNN for big data analysis.