In view of the problem that the overall time complexity of the FSDP clustering algorithm was high because the algorithm needed to traverse the entire data set when calculating the local density and minimum distance of data objects, this paper presented a Spark-based parallel FSDP clustering algorithm called SFSDP. First, the algorithm divided the dataset into multiple data partitions with relatively equal size by spatial meshing. Then, it used the improved FSDP clustering algorithm to perform the clustering analysis on the data in each partition parallelly. It generated the global clusters by grouping together local clusters between partitions. Experimental results show that SFSDP algorithm can effectively perform large-scale dataset clustering analysis compared with FSDP algorithm, and the algorithm has a good performance in terms of accuracy and scalability.

[2020-01-05] Printed Article /doc/2018.04.0377/v2