What is Special about Mining Spatial and Spatio-temporal datasets?

<strong>SCHOOL OF COMPUTER &amp; SYSTEMS SCIENCES JAWAHARLAL NEHRU UNIVERSITY</strong> SEMINAR <strong>What is Special about Mining Spatial and Spatio-temporal datasets?</strong> SPEAKER: <strong>PROF. SHASHI SHEKHAR</strong>, Dept. of Computer Science, University of Minnesota, Minneapolis, USA Date:<strong> August 21, 2015</strong> <strong>Abstract:</strong> The importance of spatial and spatio-temporal data mining is growing with the increasing incidence and importance of large datasets such as maps, virtual globes, repositories of remote-sensing images, the decennial census and collections of trajectories (e.g. gps-tracks). Classical data mining techniques often perform poorly when applied to spatial and spatio-temporal data sets because of many reasons. First, these data set are embedded in continuous space, whereas classical datasets (e.g. transactions) are often discrete. Second, patterns are often local whereas classical data mining techniques often focus on global patterns. Finally, one of the common assumptions in classical statistical analysis is that data samples are independently generated. When it comes to the analysis of spatial and spatio-temporal data, however, the assumption about the independence of samples is generally false because such data tends to be highly self-correlated. Thus new methods are needed to analyze spatial and spatio-temporal data to interesting, useful and non-trivial patterns. This talk surveys come of the new methods including those for discovering interactions (e.g. co-locations, co-occurrences, tele-connections), detecting spatial outliers and location prediction along with emerging ideas on spatio-temporal pattern mining.