DBRS+: A Density-Based Spatial Clustering Method in the Presence of Obstacles and Facilitators.

Ms. Xin Wang Department of Computer Science University of Regina

DEPARTMENT SEMINAR DATE: Wednesday, March 29, 2006 TIME: 3:00 pm PLACE: Thorvaldson 271 This is a recruitment seminar. *** Everyone is welcome ***

Abstract

Spatial clustering is the process of grouping similar objects based on their distance, connectivity, or relative density in space. Sometimes general purpose spatial clustering algorithms are not effective when applied directly to real world data, due to the presence of obstacles and facilitators. In this seminar, we introduce a constrained spatial clustering method, called DBRS+, which aims to cluster spatial data in the presence of both obstacles and facilitators. It can handle datasets with intersected obstacles and facilitators. Without preprocessing, DBRS+ processes constraints during clustering. It can find clusters with arbitrary shapes. DBRS+ has been empirically evaluated using synthetic and real data sets and its performance has been compared to all known related methods.

About the speaker

Ms. Xin Wang received her B.S. degree and M.Sc. degree in Computer Science from Northwest University in China. She has been a Ph.D. candidate at the Department of Computer Science of the University of Regina since 2000. Before coming to Canada, she worked as an instructor at the East China University of Science and Technology and as a software engineer at ASTI Shanghai Corporation. Xin has published more than ten papers in various journals and conference proceedings. Her research interests are concentrated in data mining, especially in spatial clustering and applying knowledge engineering methods to data mining.

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