Improving Scalability in a Scientific Discovery System by Exploiting Parallelism

Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997

Volumn , Issue , 1997, Pages 171-174

  Galal, Gehad ^a   Cook, Diane J ^a   Holder, Lawrence B ^a  

^a UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER AIDED DESIGN; DATA MINING; DISTRIBUTED COMPUTER SYSTEMS; GRAPHIC METHODS; SCALABILITY;

DISCOVERY SYSTEMS; DISTRIBUTED RESOURCES; GRAPH-BASED; KDD SYSTEM; KNOWLEDGE DISCOVERY AND DATA MININGS; KNOWLEDGE DISCOVERY SYSTEMS; LARGE AMOUNTS OF DATA; PROCESSING TIME; SCALINGS; SCIENTIFIC DISCOVERY;

DATABASE SYSTEMS;

EID: 33745802860     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (4)

1. Chan, P., and Stolfo, S. 1993. Toward parallel and distributed learning by meta-learning. In Working notes of the AAAI-93 workshop on Knowledge Discovery in Databases, 227-240.
2. Cook, D. J.; Holder, L. B.; and Djoko, S. 1996. Scalable discovery of informative structural concepts using domain knowledge. IEEE Expert ll(5):59-68.
3. Fayyad, U. M.; Piatetsky-Shapiro, G.; Smyth, P.; and Uthurusamy, R. 1996. Advances in Knowledge Discovery and Data Mining. Menlo Park, CA: AAAI Press.
4. Provost, F. J., and Hennessy, D. 1996. Scaling up: Distributed machine learning with cooperation. In Proceedings of AAAI, 74-79.