Amanote Research

Amanote Research

    RegisterSign In

Detection of Fraudulent Financial Statements Using the Hybrid Data Mining Approach

SpringerPlus - Germany
doi 10.1186/s40064-016-1707-6
Full Text
Open PDF
Abstract

Available in full text

Categories
Multidisciplinary
Date

January 27, 2016

Authors
Suduan Chen
Publisher

Springer Science and Business Media LLC


Related search

Fraudulent Financial Statements: Detection Modeling Using Data Mining

International Journal of Latest Trends in Engineering and Technology
2018English

An Overview of Instruments and Tools to Detect Fraudulent Financial Statements

Universal Journal of Accounting and Finance
2019English

PFrauDetector: A Parallelized Graph Mining Approach for Efficient Fraudulent Phone Call Detection

2016English

A Hybrid Image Mining Technique Using LIMbased Data Mining Algorithm

International Journal of Computer Applications
2011English

Corrosion Control Approach Using Data Mining

International Journal of Computer Science and Information Technology
2015English

Profiling Oman Education Data Using Data Mining Approach

2017English

Non-Financial Indicators of Fraudulent Bankruptcy

Accounting. Analysis. Auditing
2019English

Predicting Financial Time Series Data Using Hybrid Model

Studies in Computational Intelligence
Artificial Intelligence
2016English

Program Applications Install Fraud Detection Using Data Mining

Naukovi praci Donec'kogo nacional'nogo tehnicnogo universitetu. Seria, Informatika, kibernetika i obcisluval'na tehnika
2017English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2026 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy