Big Data, Data Mining and Machine Learning

By:

Jared Dean

Material type: Text

TextPublication details: Wiley 2014ISBN:

9788126550906

Contents:

Part One: The Computing Environment • Hardware • Distributed Systems • Analytical Tools Part Two: Turning Data into Business Value • Predictive Modeling • Common Predictive Modeling Techniques • Segmentation • Incremental Response Modeling • Time Series Data Mining • Recommendation Systems • Text Analytics Part Three: Success Stories of Putting It All Together • Case Study of a Large U.S. Based Financial Services Company • Case Study of a Major Health Care Provider • Case Study of a Technology Manufacturer • Case Study of Online Brand Management • Case Study of Mobile Application Recommendations • Case Study of a High Tech Product Manufacturer • Looking to the Future

Summary: This book provides a comprehensive view on the recent trend toward high performance computing architectures especially as it relates to analytics and data mining. Topics that are covered include: big data (and its characteristics), high performance computing for analytics, massively parallel processing (MPP) databases, algorithms for big data, in-memory databases, implementation of machine learning algorithms for big data platforms and analytics environments. However none gives a historical and comprehensive view of all these separate topics in a single document.

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Holdings
Item type	Current library	Home library	Call number	Status	Date due	Barcode
Book	Dept. of Computational Biology and Bioinformatics Processing Center	Dept. of Computational Biology and Bioinformatics	004 DEA -BIG (Browse shelf(Opens below))	Available		DCB3074
Book	Dept. of Computer Science Processing Center	Dept. of Computer Science		Checked out to Divya Raj (DCSMP2103)	16/02/2022	DCS4275

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This book provides a comprehensive view on the recent trend toward high performance computing architectures especially as it relates to analytics and data mining. Topics that are covered include: big data (and its characteristics), high performance computing for analytics, massively parallel processing (MPP) databases, algorithms for big data, in-memory databases, implementation of machine learning algorithms for big data platforms and analytics environments. However none gives a historical and comprehensive view of all these separate topics in a single document.

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