An Evaluation of AI-Driven Data Mining Techniques for Large-Scale Data Processing

Information mining and synthetic intelligence (AI) have gained a lot interest in recent years in the discipline of large-scale statistics processing. AI-driven statistics mining strategies are especially effective for identifying beneficial styles and functions in huge datasets. This paper presents an assessment of several typically used AI-pushed information mining techniques for processing big datasets and compares their performance in phrases of pace, accuracy, and scalability. Not unusual algorithms including precept component evaluation and clustering analysis are evaluated and in comparison to recent advances in AI-driven records mining which includes deep mastering and associative statistics mining. The overall performance of every technique is measured throughout 3 categories: speed, accuracy, and scalability. The effects of the evaluation show that deep learning and associative information mining outperform the conventional strategies in terms of accuracy and scalability whilst they're fairly slower than the other techniques. Therefore, these algorithms can be more suitable for positive types of massive-scale statistics processing tasks that require excessive accuracy and scalability.