ADAPTIVE FILTERING ALGORITHMS FOR MODERN SIGNAL PROCESSING SYSTEMS: DESIGN AND IMPLEMENTATION

Abstract

The rapid growth of large-scale distributed databases has created significant challenges in efficient data processing and knowledge discovery. Traditional data mining techniques often suffer from scalability, latency, and resource utilization issues when applied to massive and heterogeneous datasets. This paper proposes a novel scalable data mining approach designed to efficiently process largescale distributed databases while maintaining high accuracy and low computational overhead. The proposed model integrates distributed processing, adaptive data partitioning, and intelligent workload balancing mechanisms. Experimental evaluations are conducted on a multi-node distributed environment using real and synthetic datasets. Performance is analyzed in terms of accuracy, processing time, scalability, and resource utilization. The results demonstrate that the proposed approach significantly outperforms conventional distributed data mining techniques. The findings confirm the suitability of the proposed framework for next-generation big data applications