Euclidean distance stratified random sampling based clustering model for big data mining

Abstract

Big data mining is related to large-scale data analysis and faces computational cost-related challenges due to the exponential growth of digital technologies. Classical data mining algorithms suffer from computational deficiency, memory utilization, resource optimization, scale-up, and speed-up related challenges in big data mining. Sampling is one of the most effective data reduction techniques that reduces the computational cost, improves scalability and computational speed with high efficiency for any data mining algorithm in single and multiple machine execution environments. This study suggested a Euclidean distance-based stratum method for stratum creation and a stratified random sampling-based big data mining model using the K-Means clustering (SSK-Means) algorithm in a single machine execution environment. The performance of the SSK-Means algorithm has achieved better cluster quality, speed-up, scale-up, and memory utilization against the random sampling-based K-Means and classical K-Means algorithms using silhouette coefficient, Davies Bouldin index, Calinski Harabasz index, execution time, and speedup ratio internal measures.