b2ap3_thumbnail_Elizabeth-Wang.jpgElizabeth Wang, associate professor of computer science at Waynesburg University, will be presenting her paper titled “Fast Outlier Detection on Mixed-Attribute Data” at an international conference this spring. The 2nd Conference on Artificial Intelligence and Data Mining (AIDM 2014) will be held March 10 through March 12, 2014, in Suzhou, China.

Outlier detection is one of the primary steps in data mining applications such as fraud and intrusion detection, and clinical diagnosis.

“Data mining and fraud detection in particular are my main research interest,” said Wang. “Among the eight journals, 32 refereed conference papers and eight book chapters that I have published, more than half of them are on data mining. Constant research keeps me updated with the cutting edge researches in data mining areas.”

Though the majority of outlier detection approaches are designed for numeric or categorical datasets, Wang notes real-life data, such as business transactions and clinical records, also contain categorical and numeric datasets. The notion of developing “an outlier detection method on mixed-attribute real world data” is the main focus of Wang’s paper.

The idea to conduct this research came to Wang in the summer of 2012 and carried through to the summer of 2013. After much reading, experiments, programming and brain storming, her observations transpired into a concrete concept with which she found success.

Wang has represented Waynesburg in several international conferences and has added significant research elements to the University through her many publications. This year, in addition to presenting, Wang may also have the opportunity to serve as a session chair AIDM 2014.

Wang holds a B.E. from Beijing University of Science, an M.A. from St. John’s University, an M.S. from St. Cloud University and a Ph.D. from North Dakota State University.

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Contact: Ashley Wise, Communication Specialist
724.852.7675 or awise@waynesburg.edu