Revue Syst´ematique de la Litt´erature sur les M´ethodes d’Apprentissage Automatique pour l’Analyse des Big Data avec une ´ Etude de Cas

Abstract

With the explosion of data volume generated daily, Big Data has become a major concern across various domains. The significance of Big Data lies in its ability to provide valuable insights and facilitate informed decision-making. However, to fully harness this potential, it is essential to employ machine learning techniques that can process, analyze, and extract relevant information from these vast datasets. This thesis presents a systematic literature review on machine learning methods for Big Data processing and analysis, accompanied by a case study. The study covers various supervised, unsupervised, semi-supervised, and deep learning techniques, along with their algorithms, including SVM, regression, decision trees, convolutional neural networks (CNN), recurrent neural networks (RNN), and clustering techniques such as HDDC, SOM, FCM, and k-means. A rigorous methodology was employed to identify and evaluate relevant studies. In the case study, the k-means algorithm was applied to the Iris dataset, demonstrating its effectiveness in identifying patterns within the data. In conclusion, this systematic review has highlighted different machine learning techniques for addressing Big Data challenges and their limitations. Through this study, current issues have been identified, paving the way for exploring avenues for improvement and resolution of these issues in the future.