Synopsis
Due to digital advancements large volumes of data are being generated by the modern applications. In order to accurately categorize the data in these large datasets, clustering algorithms are used. This book presents a literature review of various traditional clustering algorithms and their comparisons from a theoretical perspective. The book also provides the survey of applications of clustering techniques on I) web log data, II) image data and III) biological data. One of the major drawbacks with the traditional clustering algorithms is that they are computationally expensive when the input datasize is too large. To overcome this problem, we also provide a comprehensive study of recent MapReduce based clustering algorithms which extend the traditional counterpart with Map-Reduce programming paradigm. Mainly this book is suitable for researchers who are interested in the field of pattern discovery from large datasets using MapReduce clustering. It will help them carrying out data clustering in distributed environment. More importantly, the issues and open areas discussed in this book will help the researchers in identifying their future direction.
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Traditional and Map-Reduce Based Clustering for Large Datasets
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LAP LAMBERT Academic Publishing Jul 2019, 2019
ISBN 10: 6200244650
ISBN 13: 9786200244659
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Due to digital advancements large volumes of data are being generated by the modern applications. In order to accurately categorize the data in these large datasets, clustering algorithms are used. This book presents a literature review of various traditional clustering algorithms and their comparisons from a theoretical perspective. The book also provides the survey of applications of clustering techniques on I) web log data, II) image data and III) biological data. One of the major drawbacks with the traditional clustering algorithms is that they are computationally expensive when the input datasize is too large. To overcome this problem, we also provide a comprehensive study of recent MapReduce based clustering algorithms which extend the traditional counterpart with Map-Reduce programming paradigm. Mainly this book is suitable for researchers who are interested in the field of pattern discovery from large datasets using MapReduce clustering. It will help them carrying out data clustering in distributed environment. More importantly, the issues and open areas discussed in this book will help the researchers in identifying their future direction. 132 pp. Englisch. Seller Inventory # 9786200244659
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Traditional and Map-Reduce Based Clustering for Large Datasets
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LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200244650
ISBN 13: 9786200244659
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ansari ZahidDr. Zahid Ansari is a Professor of CSE and Dean (Research) at PA College of Engineering, Mangalore, India. Earlier he was associated with Tata Consultancy Services R&D Center, Pune, GE-Harris Melbourne, Florida USA and US. Seller Inventory # 385886117
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Traditional and Map-Reduce Based Clustering for Large Datasets: A Systematic Review
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200244650
ISBN 13: 9786200244659
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Paperback. Condition: Brand New. 132 pages. 8.66x5.91x0.30 inches. In Stock. Seller Inventory # zk6200244650
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Traditional and Map-Reduce Based Clustering for Large Datasets
Published by
LAP LAMBERT Academic Publishing Jul 2019, 2019
ISBN 10: 6200244650
ISBN 13: 9786200244659
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Due to digital advancements large volumes of data are being generated by the modern applications. In order to accurately categorize the data in these large datasets, clustering algorithms are used. This book presents a literature review of various traditional clustering algorithms and their comparisons from a theoretical perspective. The book also provides the survey of applications of clustering techniques on I) web log data, II) image data and III) biological data. One of the major drawbacks with the traditional clustering algorithms is that they are computationally expensive when the input datasize is too large. To overcome this problem, we also provide a comprehensive study of recent MapReduce based clustering algorithms which extend the traditional counterpart with Map-Reduce programming paradigm. Mainly this book is suitable for researchers who are interested in the field of pattern discovery from large datasets using MapReduce clustering. It will help them carrying out data clustering in distributed environment. More importantly, the issues and open areas discussed in this book will help the researchers in identifying their future direction.Books on Demand GmbH, Überseering 33, 22297 Hamburg 132 pp. Englisch. Seller Inventory # 9786200244659
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Traditional and Map-Reduce Based Clustering for Large Datasets | A Systematic Review
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200244650
ISBN 13: 9786200244659
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Seller: preigu, Osnabrück, Germany
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Taschenbuch. Condition: Neu. Traditional and Map-Reduce Based Clustering for Large Datasets | A Systematic Review | Zahid Ansari (u. a.) | Taschenbuch | 132 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200244659 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 117239222
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Traditional and Map-Reduce Based Clustering for Large Datasets : A Systematic Review
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200244650
ISBN 13: 9786200244659
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Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Due to digital advancements large volumes of data are being generated by the modern applications. In order to accurately categorize the data in these large datasets, clustering algorithms are used. This book presents a literature review of various traditional clustering algorithms and their comparisons from a theoretical perspective. The book also provides the survey of applications of clustering techniques on I) web log data, II) image data and III) biological data. One of the major drawbacks with the traditional clustering algorithms is that they are computationally expensive when the input datasize is too large. To overcome this problem, we also provide a comprehensive study of recent MapReduce based clustering algorithms which extend the traditional counterpart with Map-Reduce programming paradigm. Mainly this book is suitable for researchers who are interested in the field of pattern discovery from large datasets using MapReduce clustering. It will help them carrying out data clustering in distributed environment. More importantly, the issues and open areas discussed in this book will help the researchers in identifying their future direction. Seller Inventory # 9786200244659