PARALLELIZATION METHODS OF DATA MINING ALGORITHMS: ENHANCING PERFORMANCE IN THE AGE OF BIG DATA

The exponential growth of data in recent years has presented significant challenges for traditional data mining algorithms. These algorithms, often designed for sequential processing, struggle to handle the massive datasets common in modern applications. Parallelization offers a solution by distributing the computational workload across multiple processors or machines, leading to significant improvements in efficiency and scalability. This article explores the importance of parallelization in data mining, examines common parallelization techniques, and discusses their application to popular algorithms like k-means clustering and DBSCAN, including their mathematical foundations.