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Scalable machine‐learning algorithms for big data analytics: a comprehensive review
Author(s) -
Gupta Preeti,
Sharma Arun,
Jindal Rajni
Publication year - 2016
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1194
Subject(s) - big data , computer science , scalability , machine learning , data science , analytics , marketing buzz , data analysis , artificial intelligence , data mining , database , world wide web
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 This article is categorized under: Technologies > Machine Learning