Electronic Banking Fraud Detection

de Aluko, Sayo Enoch
État : Neuf
35,30 €
TVA incluse - Livraison GRATUITE
Aluko, Sayo Enoch Electronic Banking Fraud Detection
Aluko, Sayo Enoch - Electronic Banking Fraud Detection

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Livraison : entre mardi 2 novembre 2021 et jeudi 4 novembre 2021
Vente et expédition: Dodax

La description

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.

Contributeurs

Écrivain:
Aluko, Sayo Enoch

Détails du produit

Biographie:
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.
Langues:
Anglais
Nombre de pages:
80
Type de média:
Souple
Éditeur:
LAP Lambert Academic Publishing

Données de base

Type d'produit:
Livre de poche
Date de publication:
17 octobre 2017
Dimensions du colis:
0.22 x 0.15 x 0.005 m; 0.168 kg
GTIN:
09783659916878
DUIN:
JMGOVTL80CF
35,30 €
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