Distributed DENCLUE Algorithm Based on Apache Spark

Clustering is an essential technique to extract hidden patterns and similar groups from data. Consequently, clustering as an unsupervised learning method is critical in extensive data analysis, mainly due to large numbers of unlabeled data. However, efficient processing of large datasets represents a significant challenge as data volumes increase exponentially. In this context, this study aims to enhance the DENCLUE 2.0 (DENsity-based CLUstEring) algorithm, a density-based clustering algorithm, from its sequential version to a distributed version using a distributed computing approach based on Apache Spark to address this challenge and accelerate the clustering process.