An Efficient Parallel Algorithm for Clustering Big Data based on the Spark Framework
Zineb Dafir, Said Chah Slaoui · International Journal of Advanced Computer Science and Applications · 2022
The principal objective of this paper is to provide a parallel implementation focused on the main steps of the parameter-free clustering algorithm based on K-means (PFK-means) using the Spark framework and a machine learning-based model to process Big Data. Thus, the process consists of parallelizing the main tasks of the first stage of the PFK-means clustering algorithm using successive RDD functions. Then, the parallel K-means provided by Spark MLlib is invoked by setting the cluster centers and the number of clusters determined in the previous step as input parameters of the parallel K-means. Furthermore, a comparison between the parallel designed algorithm and the parallel K-means was conducted using UCI data sets in terms of the sum of squared errors and the processing time. The experimental results, performed locally using the Spark framework, demonstrate the efficiency of the proposed solution.