Stock Crime Detection Using Graph Mining

Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
Previously existing graph mining algorithm typically assumes that database is relatively static. To overcome that the authors 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.

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