CAREER: Data Mining Techniques for Geospatial Applications

University Of California-Riverside, Riverside CA

Investigators

Abstract

The goal of this research is to develop fundamental techniques to allow efficient and interactive knowledge discovery from large multidimensional datasets with spatial and temporal attributes. There are two general research goals. The first involves the investigation of effective density approximation techniques to approximate very large geospatial datasets. The proposed techniques are used both to facilitate simple exploratory data mining tasks on large geospatial datasets, and to efficiently provide accurate approximate solutions to general data mining tasks, such as clustering, classification and outlier detection. The second involves designing and implementing new algorithms and techniques for similarity queries in geospatial datasets. The educational component of the project aims to develop courses that emphasize the fundamental ideas in data mining, and introduce students to real data mining problems. The results of this project will have a significant impact on how large multidimensional datasets are analyzed, with applications in the fields of Geographic Information Systems, Epidemiology, and Environmental research.