Wang develops parallel implementation of market basket analysis
Elizabeth Wang, Assistant Professor of Computer Science, developed a new clustering method for market-basket data analysis termed the WC-clustering method. This type of data mining analysis is used by corporations and has made the difference between becoming the world’s biggest corporation and filing for bankruptcy. Wang along with student researchers, Erik Murphy, David Patton, Rich Janicki, and Dustin Yoder, have implemented the WC-clustering method on parallel machines at the Pittsburgh Supercomputer Center speed up the clustering process. This method generates finer and hierarchical clustering results more efficiently than the traditional Association Rule Mining method or the traditional partitional clustering method.
Baoying (Elizabeth) Wang
Assistant Professor of Computer Science