MapReduce-based k-prototypes clustering method for big data

Big data clustering is one of the recently challenging tasks that is used in many application domains. Traditional clustering methods are not able to deal with large-scale of data. Furthermore, Big data are often characterized by the mixed type of data, including numerical and categorical attributes. Thus, we propose in this paper the parallelization of k-prototypes clustering method (MR-KP) using MapReduce model to handle large-scale of mixed data. Experiments results show that MR-KP scales well with increasing data set sizes and achieves a close to linear speedup while maintaining the clustering accuracy.