Scalable random sampling K-prototypes using spark

Abstract

Big data clustering has become an important challenge in machine learning. Several Big data frameworks have been developed to scale clustering methods for Big data analysis. One such framework called Spark works well for iterative algorithms by supporting in-memory computations. We propose in this paper a new Scalable Random Sampling K-Prototypes, implemented on Spark framework. This method is able to perform grouping from mixed large scale data. Experiments realized on simulated and real data sets show the efficiency of the proposed method compared to existing k-prototypes methods.