Bayesian networks for lossless dataset compression

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Bayesian networks for lossless dataset compression

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

San Diego, California, United States

Pages: 387 - 391

Year of Publication: 1999

ISBN:1-58113-143-7

Authors

Scott Davies	Carnegie Mellon University
Andrew Moore	Carnegie Mellon University

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI : Am Assoc for Artifical Intelligence
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 32, Citation Count: 3

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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	J. Carpinelli, A. Moffat, R. Neal, W. Salamonsen, L. Stuiver, and I. Witten. Word, Character, and Bit Based Compression Using Arithmetic Coding. Available for download at ftp://munnari.oz.au/pub/arith_coder/, 1995.
	2	D. Chickering. Learning Bayesian networks is NP-complete. In Learning .from Data, pages 121-130. Springer-Verlag, 1996.
	3	C. K. Chow and C. N. Liu. Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, I"I'-14:462-467, 1968.
	4	S. Davies and A. Moore. Bayesian Networks for Lossless Dataset Compression. Technical Report in Progress, CMU School of Computer Science, 1999.
	5	T. Dean and K. Kanazawa. Probabilistic temporal reasoning. In AAAL88 Proceedings, pages 524-528, 1988.
	6	Brendan J. Frey, Graphical models for machine learning and digital communication, MIT Press, Cambridge, MA, 1998
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	8	W. Lam and F. Bacchus. Learning Bayesian belief networks: an approach based on the MDL principle. Computational Intelligence, 10:269-293, 1994.
	9	A. Moffat , R. Neal, Arithmetic coding revisited, Proceedings of the Conference on Data Compression, p.202, March 28-30, 1995
	10	A. W. Moore and M. S. Lee. Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets. Journal of Artificial Intelligence Research, 8, 1998.
	11	Richard Clark Pasco, Source coding algorithms for fast data compression., 1976
	12	Judea Pearl, Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1988
	13	J. J. Rissanen. Generalized Kraft inequality and arithmetic coding. IBM Journal of Research and Development, 20:198- 203, May 1976.
	14	M. Sahami. Learning Limited Dependence Bayesian Classifters. In KDD-96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pages 335-338. AAAI Press, 1996.
	15	(3. Schwarz. Estimating the dimension of a model. Annals of Statistics, 6:461-464, 1978.
	16	Ian H. Witten , Radford M. Neal , John G. Cleary, Arithmetic coding for data compression, Communications of the ACM, v.30 n.6, p.520-540, June 1987 [doi>10.1145/214762.214771]
	17	J. Ziv. Coding theorems for individual sequences. IEEE Transactions on Information Theory, 24:389-394, 1978.
	18	J. Ziv and A. Lempel. A Universal Algorithm for Data Compression. IEEE Transactions on Information Theory, 23(3):337-343, May 1977.

CITED BY 3

	Pedro Domingos, Machine learning, Handbook of data mining and knowledge discovery, Oxford University Press, Inc., New York, NY, 2002
	Shivnath Babu , Minos Garofalakis , Rajeev Rastogi, SPARTAN: using constrained models for guaranteed-error semantic compression, ACM SIGKDD Explorations Newsletter, v.4 n.1, June 2002
	Dimitris Margaritis , Christos Faloutsos , Sebastian Thrun, NetCube: A Scalable Tool for Fast Data Mining and Compression, Proceedings of the 27th International Conference on Very Large Data Bases, p.311-320, September 11-14, 2001