A memory efficient distributed fuzzy joint points clustering algorithm

The fuzzy joint points (FJP) is a method that uses a fuzzy neighborhood notion to deal with neighborhood parameter selection issue of classical density-based clustering and offers an unsupervised clustering tool. Recent works improved the method in terms of speed to enable the method for big data applications. However, space efficiency of the method is still a limiting factor. In this work, we discuss techniques to improve the space efficiency of the method, so that FJP is applicable regardless of the size of data and offer a distributed version of the algorithm.