Deep Learning–Based Data Mining Approaches for Large-Scale Data Science Applications

Abstract

This paper presents a discussion of how big data analytics combine with machine learning and deep learning to perform large-scale data mining tasks in a variety of fields. The main aim is to examine how sophisticated computational models and smart algorithms can be used to accelerate data processing and pattern recognition as well as decision-making in complex and high-dimensional data. The methodology will be a systematic review and comparative analysis of available machine learning models, deep learning architectures as well as distributed computing tools to process large-scale data mining. Some of the techniques that are tested based on efficiency, accuracy, and scalability include supervised and unsupervised learning, neural networks, and scalable platforms such as Apache Spark. It is seen that deep learning models are much better than the traditional methods in unstructured and large-volume data, and distributed systems enhance processing speed and use of resources. Also, healthcare, bioinformatics, and smart agricultural domain-specific applications prove the effectiveness of these methods in practice. The paper finds that the intersection of the big data technologies with machine learning allows intelligent data-driven systems with a solid ground, but the issues of computational complexity, data-privacy, and model-interpretability are the areas where future research should be vital.