EFFICIENT AND SCALABLE DATA MINING TECHNIQUES FOR DISTRIBUTED BIG DATA ENVIRONMENTS

Abstract

The rapid expansion of big data platforms has intensified the need for high-performance and scalable data mining methods capable of processing massive distributed datasets efficiently. Conventional data mining techniques often fail to deliver acceptable performance due to scalability constraints and excessive computational overhead. This paper presents a high-performance scalable data mining (HP-SDM) method designed for distributed big data platforms. The proposed approach integrates parallel processing, adaptive data partitioning, and intelligent resource management to enhance execution efficiency. Extensive experiments are conducted on distributed environments using large-scale datasets. Performance is evaluated in terms of accuracy, execution time, scalability, and resource utilization. Experimental results demonstrate that the proposed method significantly outperforms existing techniques. The study confirms the suitability of the proposed approach for nextgeneration big data analytics applications