Speaker: Weili Wu , CSE Dept., University of Minnesota, Minneapolis
Modeling Spatial Dependencies for Data Mining
Abstract: Spatial data mining is a process to discover interesting, potentially useful, and high utility patterns embedded in spatial databases. Efficient tools for extracting information from spatial data sets can be of importance to organizations which own, generate, and manage large spatial data sets. The current approach towards solving spatial data mining problems is to use classical data mining tools after 'materializing' spatial relationships. However, the key property of spatial data is that of spatial autocorrelation. Like temporal data, spatial data values are influenced by values in their immediate vicinity. Ignoring spatial autocorrelation in the modeling process leads to results which are a poor-fit and unreliable.
In this talk, a new approach, PLUMS (Predicting Locations Using Map Similarity), will be presented for supervised spatial data mining problems. PLUMS searches the space of solutions using a map-similarity measure which is more appropriate in the context of spatial data. It will be showed that compared to state-of-the-art spatial statistics approaches, PLUMS achieves comparable accuracy but at a fraction of the computational cost. Furthermore, PLUMS provides a general framework for specializing other data mining techniques for mining spatial data. This work is joint with S. Shekhar et al.
Short Bio: Weili Wu received her B.S. degree in Mechanical Engineering in 1989 from Fuxin University, China. She received a M.S. degree in Economics in 1995 from University of Wisconsin and a M.S. degree in Computer Science in 1997 from University of Minnesota. She currently is Ph.D. candidate in Department of Computer Science and Engineering, University of Minnesota and is expected to receive her Ph.D. in Spring of 2002. Her main research interest is database systems. She has produced a number of research papers in spatial data mining, distributed database systems, and algorithm design. She is also a co-editor for a book in clustering and information retrieval.