Maxmin Data Range Heuristic-Based Initial Centroid Method of Partitional Clustering for Big Data Mining

The centroid-based clustering algorithm depends on the number of clusters, initial centroid, distance measures, and statistical approach central tendencies. centroid initialization defines convergence speed, computing efficiency, execution time, scalability, memory utilization, performance issues for big data clustering. Nowadays various researchers have proposed cluster techniques, where some techniques reduce iterations with lowest quality, increase quality high iterations. For these reasons, this study based Maxmin Data Range Heuristic (MDRH) method K-Means (KM) that reduces times, iterations, improves MDRH has compared against classical KM KM++ algorithms four real datasets. achieved better effectiveness efficiency over RS, DB, CH, SC, IS, CT quantitative measurements.