Optimizing K-Means Clustering Technique for Big Data Prediction Analysis

The rapid growth of big data has reshaped industries such as healthcare, finance, and e-commerce, creating an urgent need for clustering algorithms that are both scalable and efficient. While K-Means remains one of the most widely used clustering methods, it suffers from well-known limitations including sensitivity to initialization, scalability challenges, and reduced accuracy in high-dimensional spaces. This study introduces an optimized K-Means clustering framework that integrates smarter initialization strategies, dimensionality reduction techniques, and parallel processing capabilities. Together, these enhancements improve convergence speed, reduce computational overhead, and deliver higher clustering accuracy. Experimental results on both synthetic and real-world datasets demonstrate that the proposed framework not only scales effectively but also produce more reliable predictive models, establishing it as a strong candidate for modern big data analytics.