Review of Some Machine Learning Techniques for Big Data, Their Uses, Strengths and Weaknesses

Abstract

Machine learning is a field of computer science which gives computers an ability to learn without being explicitly programmed. Machine learning is used in a variety of computational tasks where designing and programming explicit algorithms with good performance is not easy.Big data are now rapidly expanding in all science and engineering domains. The potentials of these increased volumes of data are obviously very significant to every aspect of our lives. To aid us in decision making and future predictions requires new ways of thinking and new learning techniques to address the various challenges.Traditional analytical approaches are insufficient to analyze big data because they are highly scalable and unstructured data captured in real time. Machine learning (ML), addresses this challenge, by enabling a system to automatically learn patterns from data that can be leveraged in future predictions.This paper reviews some machine learning techniques for big data highlighting their uses/applications, strengths and weaknesses in learning data patterns. The techniques reviewed include Bayesian network, association rules, naïve bayes, decision trees, nearest neighbor and super vector machines (SVM)