A MapReduce-based artificial bee colony for large-scale data clustering - Banharnsakun, Anan (Pattern recognition letters, 2017)
•A MapReduce-Based ABC for Large Scale Data Clustering.•Implementing based on the MapReduce model in the Hadoop framework.•Optimizing the assignment of large data instances to clusters.•The performance in dealing with massive data is improved.•The quality level of the clustering results is still maintained. The progress of technology has been a significant factor in increasing the growth of digital data. Therefore, good data analysis is a necessity for making better decisions. Clustering is one of the most important elements in the field of data analysis. However, the clustering of very large datasets is considered a primary concern. The improvement of computational models along with the ability to cluster huge volumes of data within a reasonable amount of time is thus required. MapReduce is a powerful programming model and an associated implement for processing large datasets with a parallel, distributed algorithm in a computing cluster. In this paper, a MapReduce-based artificial bee colony called MR-ABC is proposed for data clustering. The ABC is implemented based on the MapReduce model in the Hadoop framework and utilized to optimize the assignment of the large data instances to clusters with the objective of minimizing the sum of the squared Euclidean distance between each data instance and the centroid of the cluster to which it belongs. The experimental results demonstrate that our proposed algorithm is well-suited for dealing with massive amounts of data, while the quality level of the clustering results is still maintained.