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Outliers are regarded as noisy data in statistics, has turned out to be an important problem which is being researched in diverse fields of research and application domains. Many outlier detection techniques have been developed specific to certain application domains, while some techniques are more generic. Some application domains are being researched in strict confidentiality such as research on crime and terrorist activities. The techniques and results of such techniques are not readily forthcoming.Big data analysis has become much popular in the present day scenario and the manipulation of big data has gained the keen attention of researchers in the field of data analytics. Cloud computing provides powerful and economical infrastructural resources for cloud users to handle ever-increasing Big Data with data-processing frameworks such as MapReduce.This work consider two clustering algorithms known as DBScan and K-Means and implemented with Intel Corporation's Sensed dataset.
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- EditoreLAP LAMBERT Academic Publishing
- Data di pubblicazione2018
- ISBN 10 6139820855
- ISBN 13 9786139820856
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero di pagine72
- Contatto del produttorenon disponibile
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Outlier Detection Based On Clustering Over Sensed Data Using HADOOP
Editore:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139820855
ISBN 13: 9786139820856
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mourya MorisonMr. Morison Mourya completed Master in Engineering degree in Computer Engineering from Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya, Indore in year 2016. Dr. Vaibhav Jain is Assistant Professor a. Codice articolo 385661744
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Outlier Detection Based On Clustering Over Sensed Data Using HADOOP
Editore:
LAP LAMBERT Academic Publishing Mai 2018, 2018
ISBN 10: 6139820855
ISBN 13: 9786139820856
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Outliers are regarded as noisy data in statistics, has turned out to be an important problem which is being researched in diverse fields of research and application domains. Many outlier detection techniques have been developed specific to certain application domains, while some techniques are more generic. Some application domains are being researched in strict confidentiality such as research on crime and terrorist activities. The techniques and results of such techniques are not readily forthcoming.Big data analysis has become much popular in the present day scenario and the manipulation of big data has gained the keen attention of researchers in the field of data analytics. Cloud computing provides powerful and economical infrastructural resources for cloud users to handle ever-increasing Big Data with data-processing frameworks such as MapReduce.This work consider two clustering algorithms known as DBScan and K-Means and implemented with Intel Corporation's Sensed dataset. 72 pp. Englisch. Codice articolo 9786139820856
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Outlier Detection Based On Clustering Over Sensed Data Using HADOOP
Editore:
LAP LAMBERT Academic Publishing Mai 2018, 2018
ISBN 10: 6139820855
ISBN 13: 9786139820856
Nuovo
Taschenbuch
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. Neuware -Outliers are regarded as noisy data in statistics, has turned out to be an important problem which is being researched in diverse fields of research and application domains. Many outlier detection techniques have been developed specific to certain application domains, while some techniques are more generic. Some application domains are being researched in strict confidentiality such as research on crime and terrorist activities. The techniques and results of such techniques are not readily forthcoming.Big data analysis has become much popular in the present day scenario and the manipulation of big data has gained the keen attention of researchers in the field of data analytics. Cloud computing provides powerful and economical infrastructural resources for cloud users to handle ever-increasing Big Data with data-processing frameworks such as MapReduce.This work consider two clustering algorithms known as DBScan and K-Means and implemented with Intel Corporation¿s Sensed dataset.Books on Demand GmbH, Überseering 33, 22297 Hamburg 72 pp. Englisch. Codice articolo 9786139820856
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Outlier Detection Based On Clustering Over Sensed Data Using HADOOP
Editore:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139820855
ISBN 13: 9786139820856
Nuovo
Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Outliers are regarded as noisy data in statistics, has turned out to be an important problem which is being researched in diverse fields of research and application domains. Many outlier detection techniques have been developed specific to certain application domains, while some techniques are more generic. Some application domains are being researched in strict confidentiality such as research on crime and terrorist activities. The techniques and results of such techniques are not readily forthcoming.Big data analysis has become much popular in the present day scenario and the manipulation of big data has gained the keen attention of researchers in the field of data analytics. Cloud computing provides powerful and economical infrastructural resources for cloud users to handle ever-increasing Big Data with data-processing frameworks such as MapReduce.This work consider two clustering algorithms known as DBScan and K-Means and implemented with Intel Corporation's Sensed dataset. Codice articolo 9786139820856
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