Title :
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
fDate :
Nov. 30 2009-Dec. 1 2009
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;
Conference_Titel :
Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3888-4
DOI :
10.1109/KAM.2009.221