Research on Money Laundering Detection Based on Improved Minimum Spanning Tree Clustering and Its Application

Author

Wang, Xingqi ; Dong, Guang

Author_Institution

Sch. of Comput. Sci., Hangzhou Dianzi Univ. (HDU), Hangzhou, China

Volume

2

fYear

2009

fDate

Nov. 30 2009-Dec. 1 2009

Firstpage

62

Lastpage

64

Abstract

To detect suspicious money laundering transaction in the real world financial applications, a new dissimilarity metric was proposed and a novel money laundering detection algorithm based on improved minimum spanning tree clustering was put forward in this paper. Suspicious money laundering transaction detection experiment on financial data set from the real world indicates that our algorithm is effective and succinct.

Keywords

data mining; financial data processing; trees (mathematics); dissimilarity metric; financial applications; improved minimum spanning tree clustering; money laundering detection; Algorithm design and analysis; Application software; Clustering algorithms; Computer science; Data analysis; Data mining; Detection algorithms; Forward contracts; Knowledge acquisition; Support vector machines; clustering analysis; minimum spanning tree; money laundering; outliers;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on

Conference_Location

Wuhan

Print_ISBN

978-0-7695-3888-4

Type

conf

DOI

10.1109/KAM.2009.221

Filename

5362309