Big data, data mining and machine learning : value creation for business leaders and practitioners /

"An expert guide to high performance computing architectures and how they relate to analytics and data miningWith the exponential growth of data comes an ever-increasing need to process and analyze so-called Big Data. High Performance Data Mining and Big Data Analytics provides a comprehensive...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Dean, Jared, 1978-
Μορφή: Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Hoboken, New Jersey : John Wiley and Sons, Inc., [2014]
Σειρά:Wiley and SAS business series.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Περιγραφή
Περίληψη:"An expert guide to high performance computing architectures and how they relate to analytics and data miningWith the exponential growth of data comes an ever-increasing need to process and analyze so-called Big Data. High Performance Data Mining and Big Data Analytics provides a comprehensive view of the recent trend toward high performance computing architectures and its natural connection to analytics and data mining. You'll find coverage of topics including: big data, high performance computing for analytics, massively parallel processing (MPP) databases, in-memory analytics, implementation of machine learning algorithms for big data platforms, text analytics, analytics environments, the analytics lifecycle, general applications, as well as a variety of cases. Offers coverage of business analytics, predictive modeling, and fact-based management Includes case studies featuring multinational companies Explores recent trends in high performance computing architectures relating to data mining Filled with case studies, High Performance Data Mining and Big Data Analytics provides a thorough grounding for optimally putting data mining and big data analytics to work for your organization"--
Φυσική περιγραφή:1 online resource.
Βιβλιογραφία:Includes bibliographical references and index.
ISBN:9781118920695
1118920694
9781118920701
1118920708
DOI:10.1002/9781118691786