AAAI Publications, The Thirty-Second International Flairs Conference

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Discovering Suspicious Patterns Using a Graph Based Approach
Sirisha Velampalli, Lenin Mookiah, William Eberle

Last modified: 2019-05-04

Abstract


Recently, there has been much attention on tools and techniques for visualizing and acquiring new knowledge and insights. In the VAST 2018 competition, one of the challenges is to discover the fraudulent group of employees at Kasios, a furniture manufacturing company. In this work, we use a graph-based approach that analyzes the data for suspicious employee activities at Kasios. Graph based approaches enable one to handle rich contextual data and provide a deeper understanding of data due to the ability to discover patterns in databases that are not easily found using traditional query or statistical tools. We focus on graph based knowledge discovery in structural data to mine for interesting patterns and anomalies. Our approach first reports the normative patterns in the data, and then discovers any anomalous patterns associated with the previously discovered patterns. For visualizing the suspicious patterns, we also use the enterprise graph database Neo4j. Neo4j Browser provides a way to visualize graph structures.

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