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Scalable machine-learning algorithms for big data analytics: a comprehensive review
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DOI：10.1002/widm.1194.png)
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Abstract
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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. (C) 2016 John Wiley & Sons, Ltd
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