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題目：Discovering Drivers of Change in Spatial Systems Through Association Pattern Mining

主講人：丁薇 教授 Department of Computer Science University of Massachusetts Boston

時(shí)間：2013年6月17日(周一）上午10：00-11：30

地點(diǎn)：bwin必贏(yíng)唯一官網(wǎng)313室

主持人：吳鋒 教授

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附件：

丁薇教授簡(jiǎn)歷：

Wei Ding received her Ph.D. degree ifrom the University of Houston in 2008. She has been an Assistant Professor in the University of Massachusetts Boston since 2008. Her main research interests include Big data and Data Mining, etc.. She has published more than 70 refereed research papers, 1 book, and has 1 patent. She is an Associate Editor of Knowledge and Information Systems (KAIS) and an editorial board member of the Journal of System Education (JISE). She is the recipient of a Best Paper Award at IEEE International Conference on Tools with Artificial Intelligence (ICTAI) 2011, a Best Paper Award at IEEE International Conference on Cognitive Informatics (ICCI) 2010, a Best Poster Presentation award at ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPAITAL GIS) 2008, and a Best Ph.D. Work Award between 2007 and 2010 from the University of Houston. Her research projects are currently sponsored by NASA and DOE.

講座內(nèi)容：

Advances in gathering spatial data and progress in Geographical Information Science (GIS) allow analysts to monitor and to model dynamics of complex spatial systems pertaining to geographic, economic, ecological, and natural hazards domains. Predictive models of change help to guide economic and political decisions of high social significance. This project seeks to contribute to the science of change analysis by developing a new paradigm of how spatial change is analyzed and predicted. Specifically, the project aims at creating a suite of change analysis tools that are based on data-centric and model-free foundation of association analysis instead of model-oriented tools that are presently used by change analysts. The deliverables of this project are novel algorithms that enable comprehensive and efficient analysis of major factors driving change in spatial systems and provide more accurate prediction of future change. They also represent extension to the present state-of-the-art in association analysis techniques. 

The project has an original goal and it proposes novel approaches and algorithms to achieve its purposes. The bulk of existing work on change analysis concentrates on modeling and predicting change; establishing major drivers of change is treated as a byproduct of change prediction. This project proposes a new paradigm in which comprehensive discovery of change drivers through association analysis is a central topic, and change prediction is a byproduct of the discovery process. Novel solutions are required to develop tools based on the new paradigm: (i) Change drivers are not discovered individually, but rather as discriminative patterns of multiple factors; modification of discriminative pattern mining technique to spatial datasets with ambiguous labels is proposed. (ii) Large number of change patterns need to be synthesized into more comprehensive form of knowledge; development of novel pattern similarity measure that enables such synthesis is proposed. (iii) Change prediction is based on discovered patterns of change; a technique of classification by aggregating emerging discriminative patterns is proposed to be applied to spatial datasets. Two application case studies, one pertaining to rural-urban land conversion and another to modeling hurricane risk assessment, are an integral part of this project, designed to demonstrate advantages of the new methodology for analyzing change in spatial systems.