Selective visualization of vortices in hydrodynamic flows
Proceedings of the conference on Visualization '98
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent neighboring class sets in spatial databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A mathematical theory of communication
ACM SIGMOBILE Mobile Computing and Communications Review
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
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
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
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
A visual-analytic toolkit for dynamic interaction graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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In this paper, we present efficient algorithms to discover spatial associations among features extracted from 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. We have developed algorithms to discover two types of spatial association patterns in scientific data. We present experimental results to demonstrate the efficacy of our approach on real datasets drawn from the bioinformatic domain. We also highlight the importance of the discovered patterns by integrating the underlying domain knowledge.