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Electronic Banking Fraud Detection: Using Data Mining Techniques And R Software For Implementing Machine Learning Algorithms In Prevention Of Fraud - Couverture souple
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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À propos de l?auteur
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Electronic Banking Fraud Detection
Edité par
LAP LAMBERT Academic Publishing, 2017
ISBN 10 : 3659916870
ISBN 13 : 9783659916878
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
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Etat : 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. N° de réf. du vendeur 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
Edité par
LAP LAMBERT Academic Publishing, 2017
ISBN 10 : 3659916870
ISBN 13 : 9783659916878
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 9783659916878
Quantité disponible : 1 disponible(s)
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Electronic Banking Fraud Detection
Edité par
LAP LAMBERT Academic Publishing Okt 2017, 2017
ISBN 10 : 3659916870
ISBN 13 : 9783659916878
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 9783659916878
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Electronic Banking Fraud Detection
Edité par
LAP LAMBERT Academic Publishing Okt 2017, 2017
ISBN 10 : 3659916870
ISBN 13 : 9783659916878
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 9783659916878
Quantité disponible : 2 disponible(s)
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Electronic Banking Fraud Detection: Using Data Mining Techniques And R Software For Implementing Machine Learning Algorithms In Prevention Of Fraud
Edité par
LAP LAMBERT Academic Publishing, 2017
ISBN 10 : 3659916870
ISBN 13 : 9783659916878
Neuf
Paperback
Vendeur : Revaluation Books, Exeter, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : Brand New. 80 pages. 8.66x5.91x0.19 inches. In Stock. N° de réf. du vendeur 3659916870
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