Big Data Analytics : Methods and Applications / edited by Saumyadipta Pyne, B.L.S. Prakasa Rao, S.B. Rao.
Material type:
TextEdition: 1st ed. 2016Description: (XII, 276 pages 67 illustrations)ISBN: 9788132236283Subject(s): Applied mathematics | Data mining | Engineering mathematics | Statistics | Statistics and Computing/Statistics Programs | Applications of Mathematics | Data Mining and Knowledge Discovery | Statistics for Business, Management, Economics, Finance, Insurance | Statistics for Life Sciences, Medicine, Health Sciences | Statistics for Social Sciences, Humanities, LawAdditional physical formats: Print version:: Big data analytics.; Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 005.70 Summary: This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.
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This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.
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