A survey of machine learning algorithms for big data analytics

Big data analytics is a booming research area in computer science and many other industries all over the world. It has gained great success in vast and varied application sectors. This includes social media, economy, finance, healthcare, agriculture, etc. Several intelligent machine learning techniques were designed and used to provide big data predictive analytics solutions. A literature survey of different machine learning techniques is provided in this paper. Also a study on commonly used machine learning algorithms for big data analytics is done and presented in this paper.

I. Introduction

In this data rich era it is essential to use sophisticated analytics techniques on huge, diverse big data sets to produce useful knowledge and information. Big data analytics is a budding research area that deals with the collection, storage and analysis of immense data sets to trace the unknown patterns and other key information. Big data analytics helps us to recognize the data that are integral component to the future business decisions. Big data analytics can be abundantly found in domains such as banking and insurance sector, healthcare, education, social media and entertainment industry, bioinformatics applications, geospatial applications, agriculture etc. It is a herculean task to handle big data using conventional data processing applications. Thus to discover hidden data patterns, trends and associations, intelligent machine learning methods can be adapted. The objective of the current research paper is to discuss various machine learning algorithms used by data scientists for analyzing and modeling big data.