Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 2.111

Scalable machine‐learning algorithms for big data analytics: a comprehensive review

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Big data analytics is one of the emerging technologies as it promises to provide better insights from huge and heterogeneous data. Big data analytics involves selecting the suitable big data storage and computational framework augmented by scalable machine‐learning algorithms. Despite the tremendous buzz around big data analytics and its advantages, an extensive literature survey focused on parallel data‐intensive machine‐learning algorithms for big data has not been conducted so far. The present paper provides a comprehensive overview of various machine‐learning algorithms used in big data analytics. The present work is an attempt to identify the gaps in the work already performed by researchers, thus paving the way for further quality research in parallel scalable algorithms for big data. WIREs Data Mining Knowl Discov 2016, 6:194–214. doi: 10.1002/widm.1194

Literature selection process.
[ Normal View | Magnified View ]
Lambda architecture.
[ Normal View | Magnified View ]
Big data analytics.
[ Normal View | Magnified View ]

Related Articles

Big Data with Cloud Computing: an insight on the computing environment, MapReduce , and programming frameworks
Market Basket Analysis algorithms with MapReduce
Data discretization: taxonomy and big data challenge
Top Ten WIDM Articles
WIREs at JSM 2017

Browse by Topic

Technologies > Machine Learning

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts