Proceedings of the 7th conference on Visualization '96
Numerical simulation of hydrodynamics by the method of point vortices
Journal of Computational Physics - Special issue: commenoration of the 30th anniversary
Feature detection in linked derived spaces
Proceedings of the conference on Visualization '98
Selective visualization of vortices in hydrodynamic flows
Proceedings of the conference on Visualization '98
Mining frequent neighboring class sets in spatial databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Fast mining of spatial collocations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Detection and Visualization of Anomalous Structures in Molecular Dynamics Simulation Data
VIS '04 Proceedings of the conference on Visualization '04
Qualitative Spatial Representation and Reasoning: An Overview
Fundamenta Informaticae - Qualitative Spatial Reasoning
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD Explorations Newsletter
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
On exploiting the power of time in data mining
ACM SIGKDD Explorations Newsletter
An event-based framework for characterizing the evolutionary behavior of interaction graphs
ACM Transactions on Knowledge Discovery from Data (TKDD)
Analyzing change in spatial data by utilizing polygon models
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
Towards subspace clustering on dynamic data: an incremental version of PreDeCon
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Interval-orientation patterns in spatio-temporal databases
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
MEC --Monitoring Clusters' Transitions
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Summarizing cluster evolution in dynamic environments
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II
Bipartite graphs for monitoring clusters transitions
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
On the spatiotemporal burstiness of terms
Proceedings of the VLDB Endowment
ciForager: Incrementally discovering regions of correlated change in evolving graphs
ACM Transactions on Knowledge Discovery from Data (TKDD)
Graph mining for object tracking in videos
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
FINGERPRINT: Summarizing Cluster Evolution in Dynamic Environments
International Journal of Data Warehousing and Mining
A filter-and-refine approach to mine spatiotemporal co-occurrences
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A framework to monitor clusters evolution applied to economy and finance problems
Intelligent Data Analysis
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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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.