The topic i have selected: Abstract: I will be going with the subject "Fraud detection using Data Mining" Fraud affirmation is an approach to manage shield others from being attacked by designers or to get secure from the cash desperado and cheats with the assistance of progression. Information Mining (DM) blueprint systems in recognizing firms that issue fraudulent financial statements (FFS) and manages the obvious affirmation of parts related to FFS. This evaluation explores the settlement of Decision Trees, Neural Networks, and Bayesian Belief Networks in the prominent proof of sham spending summaries. The information vector is made out of degrees got from budgetary outlines. Introduction: Data uncovering is looking for secured, significant, and possibly supportive models in huge enlightening lists. Data Mining is connected to finding unsuspected/already dark associations among the data.  It is a multi-disciplinary fitness that usages AI, estimations, AI, and database advancement. Types of data mining Relational database  Data warehouse Text mining data mining Distributed database Operational database End-user database Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert systems with applications, 32(4), 995-1003 .

1. The topic i have selected:
Abstract:
I will be going with the subject "Fraud detection using Data
Mining" Fraud affirmation is an approach to manage shield
others from being attacked by designers or to get secure from
the cash desperado and cheats with the assistance of
progression. Information Mining (DM) blueprint systems in
recognizing firms that issue fraudulent financial statements
(FFS) and manages the obvious affirmation of parts related to
FFS. This evaluation explores the settlement of Decision Trees,
Neural Networks, and Bayesian Belief Networks in the
prominent proof of sham spending summaries. The information
vector is made out of degrees got from budgetary outlines.
Introduction:
Data uncovering is looking for secured, significant, and
possibly supportive models in huge enlightening lists. Data
Mining is connected to finding unsuspected/already dark
associations among the data.
It is a multi-disciplinary fitness that usages AI, estimations, AI,
and database advancement.
Types of data mining
Relational database
Data warehouse
Text mining data mining
Distributed database

2. Operational database
End-user database
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data
mining techniques for the detection of fraudulent financial
statements. Expert systems with applications, 32(4), 995-1003