Exploiting parallelism in a structural scientific discovery system to improve scalability

Journal of the American Society for Information Science

Volumn 50, Issue 1, 1999, Pages 65-73

  Galal, Gehad M ^a  

^a The University of Texas at Arlington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ACQUISITION; DATABASE SYSTEMS; KNOWLEDGE ACQUISITION; PARALLEL PROCESSING SYSTEMS;

STRUCTURAL SCIENTIFIC DISCOVERY SYSTEMS;

DATA MINING;

EID: 0032736291     PISSN: 00028231     EISSN: None     Source Type: Journal    
DOI: 10.1002/(SICI)1097-4571(1999)50:1<65::AID-ASI8>3.0.CO;2-G     Document Type: Article

References (5)

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2. Cheeseman, P., & Stutz, J. (1996). Bayesian classification (AutoClass): Theory and results. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds.), Advances in knowledge discovery and data mining (chap. 6, pp. 153-180). Cambridge, MA: MIT Press.
3. Clearwater, S., & Provost, F. (1990). A tool for knowledge-based induction. In Proceedings of the Second International IEEE Conference on Tools for Artificial Intelligence (pp. 24-30).
4. Conklin, D., & Glasgow, J. (1992). Spatial analogy and subsumption. In D. Skeeman & P. Edwards (Eds.), Proceedings of the Machine Learning Conference Aberdeen, Scotland, (pp. 111-116).
5. Cook, D.J., & Holder, L.B. (1994). Substructure discovery using minimum description length and background knowledge. Journal of Artificial Intelligent Research, 1, 231-255.