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Big-Data Analytics, Machine Learning Algorithms and Scalable/Parallel/Distributed Algorithms

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Part of the book series: Studies in Big Data ((SBD,volume 23))

Abstract

Smart data analysis has become a challenging task in today’s environment where disparate data set is generated across the globe with enormous volume. So there is an absolute need of parallel and distributed framework along with appropriate algorithms which can handle these challenges. Various machine learning algorithms can be deployed effectively in this environment as they can work with minimal manual intervention. The objective of this chapter is first to present various issues faced in storing and processing big data and available tools, technologies and algorithms to deal with those problems along with one case study which describes an application in healthcare analytics. In the subsequent section it discusses few distributed algorithms which are widely used in the data mining domain. Finally it focuses on various machine learning algorithms and their roles in the big data analytics world.

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Correspondence to Ajanta Das .

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Desarkar, A., Das, A. (2017). Big-Data Analytics, Machine Learning Algorithms and Scalable/Parallel/Distributed Algorithms. In: Bhatt, C., Dey, N., Ashour, A. (eds) Internet of Things and Big Data Technologies for Next Generation Healthcare. Studies in Big Data, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-49736-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-49736-5_8

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