The power of sampling in knowledge discovery
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
ACM Computing Surveys (CSUR)
Staircase Failures Explained by Orthogonal Versal Forms
SIAM Journal on Matrix Analysis and Applications
VizCraft: a problem-solving environment for aircraft configuration design
Computing in Science and Engineering
Influence-based model decomposition for reasoning about spatially distributed physical systems
Artificial Intelligence
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Computer
STA: Spatio-Temporal Aggregation with Applications to Analysis of Diffusion-Reaction Phenomena
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Spatial aggregation: theory and applications
Journal of Artificial Intelligence Research
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Spatial aggregation: language and applications
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Novel runtime systems support for adaptive compositional modeling in PSEs
Future Generation Computer Systems - Special section: Complex problem-solving environments for grid computing
Gaussian process models of spatial aggregation algorithms
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Novel runtime systems support for adaptive compositional modeling in PSEs
Future Generation Computer Systems - Special section: Complex problem-solving environments for grid computing
Using hierarchical data mining to characterize performance of wireless system configurations
Advances in Engineering Software
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Data mining has traditionally focused on the task of drawing inferences from large data sets. However, many scientific and engineering domains, such as fluid dynamics and aircraft design, are characterized by scarce data, due to the expense and complexity of associated experiments and simulations. In such data-scarce domains, it is advantageous to focus the data collection effort on only those regions deemed most important to support a particular data mining objective. This article describes a mechanism that interleaves bottom-up data mining, to uncover multilevel structures in spatial data, with top-down sampling, to clarify difficult decisions in the mining process. The mechanism exploits relevant physical properties, such as continuity, correspondence, and locality, in a unified framework. This leads to effective mining and sampling decisions that are explainable in terms of domain knowledge and data characteristics. This approach is demonstrated in two diverse applications-mining pockets in spatial data, and qualitative determination of Jordan forms of matrices.