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Electronic Banking Fraud Detection: Using Data Mining Techniques And R Software For Implementing Machine Learning Algorithms In Prevention Of Fraud - Brossura
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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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Informazioni sull'autore
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. Condizione: 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. Codice articolo 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
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LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3659916870
ISBN 13: 9783659916878
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9783659916878
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Electronic Banking Fraud Detection
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ISBN 13: 9783659916878
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Condizione: 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. Codice articolo 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
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LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3659916870
ISBN 13: 9783659916878
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Paperback. Condizione: Brand New. 80 pages. 8.66x5.91x0.19 inches. In Stock. Codice articolo 3659916870
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Electronic Banking Fraud Detection
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LAP LAMBERT Academic Publishing Okt 2017, 2017
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ISBN 13: 9783659916878
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch. Codice articolo 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
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ISBN 13: 9783659916878
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Taschenbuch. Condizione: Neu. Electronic Banking Fraud Detection | Using Data Mining Techniques And R Software For Implementing Machine Learning Algorithms In Prevention Of Fraud | Sayo Enoch Aluko | Taschenbuch | 80 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783659916878 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 110381760
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