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Sinopsis
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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- EditorialLAP Lambert Academic Publishing
- Año de publicación2018
- ISBN 10 6139820855
- ISBN 13 9786139820856
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas72
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Outlier Detection Based On Clustering Over Sensed Data Using HADOOP
Publicado por
LAP LAMBERT Academic Publishing Mai 2018, 2018
ISBN 10: 6139820855
ISBN 13: 9786139820856
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9786139820856
Cantidad disponible: 2 disponibles
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Outlier Detection Based On Clustering Over Sensed Data Using HADOOP
Publicado por
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139820855
ISBN 13: 9786139820856
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9786139820856
Cantidad disponible: 1 disponibles
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Outlier Detection Based On Clustering Over Sensed Data Using HADOOP
Publicado por
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139820855
ISBN 13: 9786139820856
Nuevo
Tapa blanda
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: 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. Nº de ref. del artículo: 385661744
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Outlier Detection Based On Clustering Over Sensed Data Using HADOOP
Publicado por
LAP LAMBERT Academic Publishing Mai 2018, 2018
ISBN 10: 6139820855
ISBN 13: 9786139820856
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9786139820856
Cantidad disponible: 2 disponibles