Machine Learning Algorithms for Big Data Mining Processing: A review

Abstract

Big data mining is an excellent source of information and knowledge from systems to end users. However, managing such amounts of data or knowledge requires automation, which leads to serious consideration of the use of machine learning algorithms. Machine learning helps us make decisions if there is no right way to solve a problem identified in previous knowledge bases, and that is, too, one of the most widely used analysis and modeling tools for this purpose. In this work, we present an in-depth study that helps us to choose the best machine learning algorithms in order to process big data and extract knowledge from it, so that, this treatment can be very flexible, either in a simple system with sequential computing, or in a distributed system with parallel computing. To achieve this, we will, first and foremost, test the accuracy of the results provided by the classifiers; here we mean the strength and flexibility of a classifier when it comes to dealing with big data mining. Second, we will also test the execution speed for each classifier in complex cases; that is, when the classifier will not be sufficient to solve a particular problem in the context of big data mining, especially if all cases are dealt with quickly and efficiently. The results obtained in this paper demonstrated the superiority of certain classifiers over others in certain cases, and demonstrated their failure in other cases, the reason being due to the nature of the dataset, in particular the number of instances, the number of attributes , and the number of classes.