Data mining is the process of discovering hidden and meaningful knowledge in a data set. It has been successfully applied to many real-life problems, for instance, web personalization, network intrusion detection, and customized marketing. Recent advances in computational sciences have led to the application of data mining to various scientific domains, such as astronomy and bioinformatics, to facilitate the understanding of different scientific processes in the underlying domain.

In this thesis work, we focus on designing and applying data mining techniques to analyze spatial and spatiotemporal data originated in scientific domains. Examples of spatial and spatio-temporal data in scientific domains include data describing protein structures and data produced from protein folding simulations, respectively. Specifically, we have proposed a generalized framework to effectively discover different types of spatial and spatio-temporal patterns in scientific data sets. Such patterns can be used to capture a variety of interactions among objects of interest and the evolutionary behavior of such interactions. We have applied the framework to analyze data originated in the following three application domains: bioinformatics, computational molecular dynamics, and computational fluid dynamics. Empirical results demonstrate that the discovered patterns are meaningful in the underlying domain and can provide important insights into various scientific phenomena.