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Stock Crime Detection using Graph Mining

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 90 - Number 10
Year of Publication: 2014
Jigyasha Arora
Pawan Kumar Mishra
Prakash Joshi

Jigyasha Arora, Pawan Kumar Mishra and Prakash Joshi. Article: Stock Crime Detection using Graph Mining. International Journal of Computer Applications 90(10):4-9, March 2014. Full text available. BibTeX

	author = {Jigyasha Arora and Pawan Kumar Mishra and Prakash Joshi},
	title = {Article: Stock Crime Detection using Graph Mining},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {90},
	number = {10},
	pages = {4-9},
	month = {March},
	note = {Full text available}


Previously existing graph mining algorithm typically assumes that database is relatively static. To overcome that we proposed a new algorithm which deals with large database including the features which captures the properties of graph in few parameters and check the relationship among them in both left as well as right direction, thus adopting DFS as well as BFS approach. It further finds the sub graph by traversing the graph and extracting the desired pattern. The proposed algorithm is used for detection of crime in stock market by capturing the properties and identifying the relationship & associations that may exist between the person involved in that crime which prevent several crimes that might occur in future. We have used the ECLIPSE for the implementation of proposed algorithm and Neo4j is the graph database used for analysis.


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