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Electronic Banking Fraud Detection: Using Data Mining Techniques And R Software For Implementing Machine Learning Algorithms In Prevention Of Fraud - Softcover
Synopsis
This research work deals with the procedures for computing the presence of outliers using various distance measures and general detection performance for unsupervised machine learning, such as the K-Mean Clustering Analysis and Principal Component Analysis. A comprehensive evaluation of Data Mining Techniques, Machine Learning and Predictive modelling for Unsupervised Anomaly Detection Algorithms on Electronic Banking Transaction data sets record for over a period of six (6) months, April to September, 2015, consisting of 9 variable data fields and 8,641 observations, were used to carry out the survey on fraud detection. On completion of the underlying system, I can conclude that integrated techniques system provide better performance efficiency than a singular system. Besides, in near real-time settings, if a faster computation is required for larger data sets, just like the unlabelled data sets used for this research work, clustering based method is preferred to classification model.
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About the Author
Enoch Sayo Aluko, a CIE Examiner and Assessment Specialist attended University of Lagos, where he obtained B.Sc, in Education Mathematics and M.Sc., in Statistics. Besides, he has Diploma in Data Mining (SIIT) and a Certificate Course in Data Management and Visualization (Wesleyan University). He is a member of the Nigeria Mathematical Society.
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Electronic Banking Fraud Detection
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LAP LAMBERT Academic Publishing Okt 2017, 2017
ISBN 10: 3659916870
ISBN 13: 9783659916878
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This research work deals with the procedures for computing the presence of outliers using various distance measures and general detection performance for unsupervised machine learning, such as the K-Mean Clustering Analysis and Principal Component Analysis. A comprehensive evaluation of Data Mining Techniques, Machine Learning and Predictive modelling for Unsupervised Anomaly Detection Algorithms on Electronic Banking Transaction data sets record for over a period of six (6) months, April to September, 2015, consisting of 9 variable data fields and 8,641 observations, were used to carry out the survey on fraud detection. On completion of the underlying system, I can conclude that integrated techniques system provide better performance efficiency than a singular system. Besides, in near real-time settings, if a faster computation is required for larger data sets, just like the unlabelled data sets used for this research work, clustering based method is preferred to classification model. 80 pp. Englisch. Seller Inventory # 9783659916878
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Electronic Banking Fraud Detection
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ISBN 10: 3659916870
ISBN 13: 9783659916878
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Aluko Sayo EnochEnoch Sayo Aluko, a CIE Examiner and Assessment Specialist attended University of Lagos, where he obtained B.Sc, in Education Mathematics and M.Sc., in Statistics. Besides, he has Diploma in Data Mining (SIIT) and a C. Seller Inventory # 385770758
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Electronic Banking Fraud Detection: Using Data Mining Techniques And R Software For Implementing Machine Learning Algorithms In Prevention Of Fraud
Published by
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3659916870
ISBN 13: 9783659916878
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Paperback. Condition: Brand New. 80 pages. 8.66x5.91x0.19 inches. In Stock. Seller Inventory # 3659916870
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Electronic Banking Fraud Detection
Published by
LAP LAMBERT Academic Publishing Okt 2017, 2017
ISBN 10: 3659916870
ISBN 13: 9783659916878
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. Neuware -This research work deals with the procedures for computing the presence of outliers using various distance measures and general detection performance for unsupervised machine learning, such as the K-Mean Clustering Analysis and Principal Component Analysis. A comprehensive evaluation of Data Mining Techniques, Machine Learning and Predictive modelling for Unsupervised Anomaly Detection Algorithms on Electronic Banking Transaction data sets record for over a period of six (6) months, April to September, 2015, consisting of 9 variable data fields and 8,641 observations, were used to carry out the survey on fraud detection. On completion of the underlying system, I can conclude that integrated techniques system provide better performance efficiency than a singular system. Besides, in near real-time settings, if a faster computation is required for larger data sets, just like the unlabelled data sets used for this research work, clustering based method is preferred to classification model.Books on Demand GmbH, Überseering 33, 22297 Hamburg 80 pp. Englisch. Seller Inventory # 9783659916878
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Electronic Banking Fraud Detection : Using Data Mining Techniques And R Software For Implementing Machine Learning Algorithms In Prevention Of Fraud
Published by
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3659916870
ISBN 13: 9783659916878
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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 - This research work deals with the procedures for computing the presence of outliers using various distance measures and general detection performance for unsupervised machine learning, such as the K-Mean Clustering Analysis and Principal Component Analysis. A comprehensive evaluation of Data Mining Techniques, Machine Learning and Predictive modelling for Unsupervised Anomaly Detection Algorithms on Electronic Banking Transaction data sets record for over a period of six (6) months, April to September, 2015, consisting of 9 variable data fields and 8,641 observations, were used to carry out the survey on fraud detection. On completion of the underlying system, I can conclude that integrated techniques system provide better performance efficiency than a singular system. Besides, in near real-time settings, if a faster computation is required for larger data sets, just like the unlabelled data sets used for this research work, clustering based method is preferred to classification model. Seller Inventory # 9783659916878
Quantity: 1 available