Mining scientific data

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Mining scientific data

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Communications of the ACM archive
Volume 39 , Issue 11 (November 1996) table of contents

Pages: 51 - 57

Year of Publication: 1996

ISSN:0001-0782

Authors

Usama Fayyad	Microsoft and Jet Propulsion Laboratory, California Institute of Technology
David Haussler	University of California, Santa Cruz
Paul Stolorz	Jet Propulsion Laboratory, California Institute of Technology

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 19, Downloads (12 Months): 130, Citation Count: 15

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Burl, M.C., Fayyad, U., Perona, P., Smyth, P., and Burl, M.P. Automating the hunt for volcanoes on Venus. In Proceedings of Computer Vision and Pattern Recognition Conference (CVPR-94) (Seattle 1994). IEEE Computer Science Press, Los Mamitos, Calif., 1994, pp. 302-308.
	2	Chothia, C. One thousand families for the molecular biologist. Nature 35 7 (1992), 543-544.
	3	Usama M. Fayyad , Gregory Piatetsky-Shapiro , Padhraic Smyth , Ramasamy Uthurusamy, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, 1996
	4	Kennefick, J.D., DeCarvalho, R.R., Djorgovski, S.G., Wilber, M.M., Dickinson, E.S., Weir, N., Fayyad, U., and Roden, J. Astron. J. 110, 1 (1995), 78-86.
	5	Krogh, A., Brown, M., Mian, I.S., Sjolander, K., and Haussler, D. Hidden Markov models in computational biology: Applications to protein modeling.J. Mol. Biol. 235 (1994), 1501-1531.
	6	Krogh, A., Mian, I.S., and Haussler, D. A hidden Markov model that finds genes in E. coli DNA. Nucleic Acids Res. 22 (1994), 4768-4778.
	7	David Kulp , David Haussler , Martin G. Reese , Frank H. Eeckman, A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA, Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology, p.134-142, June 12-15, 1996
	8	Rabiner, L.R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. 1EEE 77 (1989), 257-286.
	9	Snyder, E.E., and Stormo, G.D. Identification of coding regions in genomic DNA sequences: An application of dynamic programming and neural networks. Nucleic Acids Res. 21 (1993), 607-613.
	10	Stolorz, P., and Dean, C. Quakefinder: A scalable data mining system for detecting earthquakes from space. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (Portland, Oreg., 1996), AAAI Press, Menlo Park, Calif., 1996.
	11	Stolorz, P., Nakamura, H. Mesrobian, E., Muntz, R.R., Shek, E.C., Mechoso, C.R., Farrara, J.D. Fast spatiotemporal data mining of large, geophysical datasets. In Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining (Montrdal, Aug. 1995), AAAI Press, Menlo Park, Calif. 1995, pp. 300-305.
	12	Weir, N., Fayyad, U.M., and Djorgovski, S.G. Automated star/galaxy classification for digitized POSS-II. Astron. J. 109, 6 (1995), 2401-2412.
	13	Weir, N., Djorgovski, S.G., and Fayyad, U.M. Initial galaxy counts from digitized POSS-II. Astron. J. 110, 1 (1995), 1-20.

CITED BY 15

	Stephan Rudolph , Peter Hertkorn, Data mining in scientific data, Data mining for design and manufacturing: methods and applications, Kluwer Academic Publishers, Norwell, MA, 2001
	Karim K. Hirji, Exploring data mining implementation, Communications of the ACM, v.44 n.7, p.87-93, July 2001
	Yongwon Lee , Bruce G. Buchanan , John M. Aronis, Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity, Machine Learning, v.30 n.2-3, p.217-240, Feb./ March, 1998
	Aiichiro Nakano , Rajiv K. Kalia , Priya Vashishta, Scalable Molecular-Dynamics, Visualization, and Data-Management Algorithms for Materials Simulations, Computing in Science and Engineering, v.1 n.5, p.39-47, September 1999
	Chao Wang , Srinivasan Parthasarathy, Parallel algorithms for mining frequent structural motifs in scientific data, Proceedings of the 18th annual international conference on Supercomputing, June 26-July 01, 2004, Malo, France
	Qiaozhu Mei , Chao Liu , Hang Su , ChengXiang Zhai, A probabilistic approach to spatiotemporal theme pattern mining on weblogs, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland
	Usama Fayyad, Knowledge discovery in databases: An overview, Relational Data Mining, Springer-Verlag New York, Inc., New York, NY, 2001
	George Gigli , íloi Bossé , George A. Lampropoulos, An optimized architecture for classification combining data fusion and data-mining, Information Fusion, v.8 n.4, p.366-378, October, 2007
	Usama M. Fayyad, Editorial, Data Mining and Knowledge Discovery, v.1 n.1, p.5-10, 1997
	Johannes Gehrke , Raghu Ramakrishnan , Venkatesh Ganti, RainForest—A Framework for Fast Decision Tree Construction of Large Datasets, Data Mining and Knowledge Discovery, v.4 n.2-3, p.127-162, July 2000
	Paul Stolorz , Ron Musick, Editorial, Data Mining and Knowledge Discovery, v.1 n.4, p.339-341, December 1997
	Taghi M. Khoshgoftaar , Edward B. Allen , Wendell D. Jones , John P. Hudepohl, Data Mining of Software Development Databases, Software Quality Control, v.9 n.3, p.161-176, November 2001
	Zippy Erlich , Roy Gelbard , Israel Spiegler, Data Mining by Means of Binary Representation: A Model for Similarity and Clustering, Information Systems Frontiers, v.4 n.2, p.187-197, July 2002
	Dan Judd , Philip K. McKinley , Anil K. Jain, Large-Scale Parallel Data Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.20 n.8, p.871-876, August 1998
	Usama Fayyad, Data mining, Encyclopedia of Computer Science, 4th edition, John Wiley and Sons Ltd., Chichester, 2003