document: A generalized framework for mining spatio-temporal patterns in scientific data In this paper , we present a general framework to discover spatial associations and spatio-temporal episodes for scientific datasets . In contrast to previous work in this area , features are modeled as geometric objects rather than points . We define multiple distance metrics that take into account objects ' extent and thus are more robust in capturing the influence of an object on other objects in spatial neighborhood . We have developed algorithms to discover four different types of spatial object interaction ( association ) patterns . We also extend our approach to accommodate temporal information and propose a simple algorithm to derive spatio-temporal episodes . We show that such episodes can be used to reason about critical events . We evaluate our framework on real datasets to demonstrate its efficacy . The datasets originate from two different areas : Computational Molecular Dynamics and Computational Fluid Flow . We present results highlighting the importance of the identified patterns and episodes by using knowledge from the underlying domains . We also show that the proposed algorithms scale linearly with respect to the dataset size .
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