Extending naïve Bayes classifiers using long itemsets

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Extending naïve Bayes classifiers using long itemsets

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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: 165 - 174

Year of Publication: 1999

ISBN:1-58113-143-7

Authors

Dimitris Meretakis	Computer Science Department, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Beat Wüthrich	Computer Science Department, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

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

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ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 9, Downloads (12 Months): 70, Citation Count: 22

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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.

	A97	David W. Aha, Lazy learning, Lazy learning, Kluwer Academic Publishers, Norwell, MA, 1997
	AS94	Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
	AMS97	K.AIi, S. Manganaris, R. Srikant, "Partial Classification using Association Rules", Proc. 3r~ Int'l Conf. on Knowledge Discovery & Data Mining, 1997.
	B97	R.J.Bayardo, "Brute-Force Mining of High Confidence Classification Rules", Proc. 3rd Int'l Conf. on Knowledge Discovery & Data Mining, 123-126, 1997.
	B+96	C. Boutilier, N.Friedman,M.Goldszmidt,D.Koller, "Context-Specific Independence in Bayesian Networks, In Proc. Twelfth Conf. on Uncertainty in Artificial Intelligence (UAD. 1996.
	CL68	C.K.Chow, C.N.Liu, "Approximating discrete probability distributions with dependence trees", IEEE trans, on Information Theory, 14, 462-467, 1968.
	DP97	Pedro Domingos , Michael Pazzani, On the Optimality of the Simple Bayesian Classifier under Zero-One Loss, Machine Learning, v.29 n.2-3, p.103-130, Nov./Dec. 1997 [doi>10.1023/A:1007413511361]
	DH73	R.Duda, P.Hart, Pattern Classification and Scene Analysis, New York: John Wiley & Sons, 1973.
	FGG97	Nir Friedman , Dan Geiger , Moises Goldszmidt, Bayesian Network Classifiers, Machine Learning, v.29 n.2-3, p.131-163, Nov./Dec. 1997 [doi>10.1023/A:1007465528199]
	FI93	U.M.Fayyad, K.B.Irani, "Multi-Interval discretization of continuous-valued attributes for classification learning", Proc. of the 13th International Joint Conference on Artificial Intelligence (IJCAD, 1022-1027, 1993.
	GFC98	G.Graefe, U.Fayyad, S.Chaudhuri, "On the Efficient Gathering of Sufficient Statistics for Classification from Large SQL Databases", Proc. 4th Int'l Conf. on Knowledge Discovery & Data Mining, 1998.
	H91	David E. Heckermann, Probabilistic similarity networks, MIT Press, Cambridge, MA, 1991
	K96	R.Kohavi, "Scaling up the accuracy of naive-Bayes classifiers: A decision-tree hybrid", Proc. 2~ lnt'l Conf. on Knowledge Discovery & Data Mining, 202-207, 1996.
	K+94	R.Kohavi, G.John, R.Long, D.Manley, K.Pfleger, "MLC++: a machine learning library in C++", Tools with Artificial Intelligence, 740-743, 1994.
	K91	Igor Kononenko, Semi-naive Bayesian classifier, Proceedings of the European working session on learning on Machine learning, p.206-219, March 1991, Porto, Portugal
	L59	P.M. Lewis, "Approximating Probability Distributions to Reduce Storage Requirements", Information and Control, 2:214-225, 1959.
	LHM98	B.Liu, W.Hsu, Y.Ma, "Integrating Classification and Association Rule Mining", Proc. 4~h lnt'l Conf. on Knowledge Discovery & Data Mining, 1998.
	LS94	P.Langley, S.Sage, "induction of selective Bayesian classifiers", Proc. 10th Conf. On Uncertainty in Artificial Intelligence, 399-406, 1994.
	MM96	C.J.Merz, P.Murphy, UCi repository of machine learning databases, 1996 (http: :llwww.cs.uci.edu/~mlearnIMLRepository.html)
	MT96	H.Mannila, H.Toivonen, "Multiple uses of frequent sets and condensed representations", Proc. 2~a Int'l Conf. on Knowledge Discovery & Data Mining, 1996.
	N87	T. Niblett, "Constructing Decision trees in noisy domains", Proc. of the Second European Working Session on Learning, (pp 67-78), 1987.
	P91	Judea Pearl, Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1988
	Q93	J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
	S96	M.Sahami, "Learning limited dependence Bayesian Classifiers Proc. 2na lnt'l Conf. on Knowledge Discovery & Data Mining, 1996.
	W94	D.H.Wolpert, "The relationship between PAC, the statistical physics framework, the Bayesian framework, and the VC framework.", In D.H.Wolpert (ed.), The Mathematics of Generalization, 1994.
	WP98	Geoffrey I. Webb , Michael J. Pazzani, Adjusted Probability Naive Bayesian Induction, Selected papers from the 11th Australian Joint Conference on Artificial Intelligence on Advanced Topics in Artificial Intelligence, p.285-295, July 13-17, 1998

CITED BY 22

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	Ke Wang , Senqiang Zhou , Yu He, Growing decision trees on support-less association rules, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, p.265-269, August 20-23, 2000, Boston, Massachusetts, United States
	Dimitris Meretakis , Dimitris Fragoudis , Hongjun Lu , Spiros Likothanassis, Scalable association-based text classification, Proceedings of the ninth international conference on Information and knowledge management, p.5-11, November 06-11, 2000, McLean, Virginia, United States
	Jinyan Li , Guozhu Dong , Kotagiri Ramamohanarao , Limsoon Wong, DeEPs: A New Instance-Based Lazy Discovery and Classification System, Machine Learning, v.54 n.2, p.99-124, February 2004
	Jiuyong Li , Rodney Topor , Hong Shen, Construct robust rule sets for classification, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada
	Fernando Berzal , Juan-Carlos Cubero , Daniel Sánchez , José María Serrano, ART: A Hybrid Classification Model, Machine Learning, v.54 n.1, p.67-92, January 2004
	Ke Wang , Yu He , Jiawei Han, Pushing Support Constraints Into Association Rules Mining, IEEE Transactions on Knowledge and Data Engineering, v.15 n.3, p.642-658, March 2003
	Liu Hongyan , Lu Hongjun , Chen Jian, A fast scalable classifier tightly integrated with RDBMS, Journal of Computer Science and Technology, v.17 n.2, p.152-159, March 2002
	Bing Liu , Yiming Ma , Ching Kian Wong, Scoring and ranking the data using association rules, Data mining, rough sets and granular computing, Physica-Verlag GmbH, Heidelberg, Germany, 2002
	Hongjian Fan , Kotagiri Ramamohanarao, A Bayesian approach to use emerging patterns for classification, Proceedings of the fourteenth Australasian database conference, p.39-48, February 01, 2003, Adelaide, Australia
	Zhongzhi Shi , Ping Luo , Yalei Hao , Guohe Li , Markus Stumptner , Qing He , Gerald Quirchmayr, Intelligent technology for well logging analysis, Intelligent information processing II, Springer-Verlag, London, 2004
	FADI THABTAH, A review of associative classification mining, The Knowledge Engineering Review, v.22 n.1, p.37-65, March 2007
	Roman Tesar , Vaclav Strnad , Karel Jezek , Massimo Poesio, Extending the single words-based document model: a comparison of bigrams and 2-itemsets, Proceedings of the 2006 ACM symposium on Document engineering, October 10-13, 2006, Amsterdam, The Netherlands
	S. Jaillet , A. Laurent , M. Teisseire, Sequential patterns for text categorization, Intelligent Data Analysis, v.10 n.3, p.199-214, January 2006
	Guoqing Chen , Hongyan Liu , Lan Yu , Qiang Wei , Xing Zhang, A new approach to classification based on association rule mining, Decision Support Systems, v.42 n.2, p.674-689, November 2006
	Lior Rokach , Oded Maimon, Feature set decomposition for decision trees, Intelligent Data Analysis, v.9 n.2, p.131-158, March 2005
	Ke Wang , Yu He , Jiawei Han, Mining Frequent Itemsets Using Support Constraints, Proceedings of the 26th International Conference on Very Large Data Bases, p.43-52, September 10-14, 2000
	Hongjun Lu , Hongyan Liu, Decision Tables: Scalable Classification Exploring RDBMS Capabilities, Proceedings of the 26th International Conference on Very Large Data Bases, p.373-384, September 10-14, 2000
	Ferenc Peter Pach , Attila Gyenesei , Janos Abonyi, Compact fuzzy association rule-based classifier, Expert Systems with Applications: An International Journal, v.34 n.4, p.2406-2416, May, 2008
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