A Synthesised Study on Data Mining and Clustering Algorithms in Cloud Computing

Abstract

Data mining and clustering algorithms have revolutionized cloud computing by enabling efficient processing, analysis, and management of massive datasets in distributed environments. This comprehensive review synthesizes key data mining techniques and clustering algorithms, including k-means, DBSCAN, and hierarchical clustering, and their applications in optimizing cloud resource allocation, anomaly detection, and big data analytics. We explore algorithmic efficiency, scalability, and seamless integration with cloud platforms, emphasizing reduced computational costs, enhanced data insights, and improved system performance. Our approach combines an extensive literature review with practical case studies on cloud-based deployments across multiple sectors. Applications in e-commerce for customer segmentation, healthcare for predictive analytics, and IoT for real-time data processing highlight the algorithms’ adaptability, robustness, and performance. Traditional data processing systems often incur 40-60% latency overheads due to sequential processing, whereas cloud-based clustering algorithms reduce processing times by 35-50% while achieving 90-95% accuracy in pattern detection. Challenges include data privacy, computational complexity, algorithm optimization for dynamic cloud environments, and interoperability across platforms. This work underscores the transformative potential of data mining and clustering to enhance scalability, intelligence, and sustainability in cloud computing, fostering innovative solutions for big data challenges.