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Computer chess compendium
Case-based reasoning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Machine Learning
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets
Data Mining and Knowledge Discovery
Machine Learning
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Similarity Search in Multimedia Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Learning semantics-preserving distance metrics for clustering graphical data
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
Apriori algorithm and game-of-life for predictive analysis in materials science
International Journal of Knowledge-based and Intelligent Engineering Systems - Soft Computing and its Applications to E-Business
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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Scientific data is often analyzed in the context of domain-specific problems, for example, failure diagnostics, predictive analysis, and computational estimation. These problems can be solved using approaches such as mathematical models or heuristic methods. In this paper we compare a heuristic approach based on mining stored data with a mathematical approach based on applying state-of-the-art formulae to solve an estimation problem. The goal is to estimate results of scientific experiments given their input conditions. We present a comparative study based on sample space, time complexity, and data storage with respect to a real application in materials science. Performance evaluation with real materials science data is also presented, taking into account accuracy and efficiency. We find that both approaches have their pros and cons in computational estimation. Similar arguments can be applied to other scientific problems such as failure diagnostics and predictive analysis. In the estimation problem in this paper, heuristic methods outperform mathematical models.