Advanced Data Mining and Deep Learning Techniques for Scalable Big Data Analytics Applications

Abstract

The paper discusses the involvement of machine learning (ML) and deep learning (DL) methods in big data analytics in order to improve scalable data processing and decision selection. The main goal is to examine how effective the high quality of analytical models are to process the large, multi-faceted, and heterogeneous data and point out the most crucial challenges and opportunities. The methodology will include an extensive review of the existing ML and DL algorithms including supervised, unsupervised, and hybrid algorithms implemented on a large scale dataset in a variety of areas. Some of the performance measures examined using experimental analysis include accuracy, scalability, and computational efficiency. The findings show that deep learning algorithms could help to enhance predictive performance and pattern recognition features of machine learning, and scalable machine learning methods could facilitate the processing of large amounts of data in a distributed setup. Nonetheless, there are also critical challenges such as the high cost of computation, quality of data, and interpretability of the model. It provides a conclusion that the combination of optimized algorithms and scalable architectures can significantly improve the work of big data analytics and guide the real-time decision making system. The work in the future should be aimed at creating lightweight models and enhancing their interpretability to be used extensively.