Machine Learning for Big Data Analytics

Ümit Demirbaga, Gagangeet Singh Aujla (), Anish Jindal () and Oğuzhan Kalyon ()
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Ümit Demirbaga: University of Cambridge, Department of Medicine
Gagangeet Singh Aujla: Durham University, Department of Computer Science
Anish Jindal: Durham University, Department of Computer Science
Oğuzhan Kalyon: Newcastle University, Faculty of Medical Sciences

Chapter Chapter 9 in Big Data Analytics, 2024, pp 193-231 from Springer

Abstract: Abstract This insightful chapter delves deeply into the enormous possibilities of using machine learning to extract meaningful insights from large amounts of data, which meticulously dissects the realm of supervised machine learning for big data analytics, unravelling the challenges inherent in its application and elucidating pre-processing methodologies essential for optimal outcomes. A comprehensive array of popular supervised machine learning algorithms is scrutinised, including Linear Regression, Logistic Regression, Decision Tree, Random Forest, Support Vector Machines, Naïve Bayes Classifier, and K-Nearest Neighbour. Transitioning seamlessly, the chapter navigates the landscape of unsupervised machine learning, shedding light on diverse techniques such as K-means Clustering, Hierarchical Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Models, Principal Component Analysis, t-distributed Stochastic Neighbour Embedding (t-SNE), Apriori Algorithm, Isolation Forest, and Expectation-Maximisation. The chapter culminates by venturing into neural network algorithms, probabilistic learning fundamentals, and performance evaluation and optimisation techniques, providing a holistic panorama of machine learning paradigms tailored to the challenges of big data analytics.

Date: 2024
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DOI: 10.1007/978-3-031-55639-5_9

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