Mining features for sequence classification

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Mining features for sequence classification

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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: 342 - 346

Year of Publication: 1999

ISBN:1-58113-143-7

Authors

Neal Lesh	MERL - Mitsubishi Electric Research Laboratory, 201 Broadway, 8th Floor, Cambridge, MA
Mohammed J. Zaki	Computer Science Dept., Rensselaer Polytechnic Institute, Troy, NY
Mitsunori Ogihara	Computer Science Dept., U. of Rochester, Rochester, NY

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): 12, Downloads (12 Months): 92, Citation Count: 14

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references cited by index terms collaborative colleagues

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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.

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CITED BY 14

	Mohammed J. Zaki, Sequence mining in categorical domains: incorporating constraints, Proceedings of the ninth international conference on Information and knowledge management, p.422-429, November 06-11, 2000, McLean, Virginia, United States
	Mukund Deshpande , George Karypis, Using conjunction of attribute values for classification, Proceedings of the eleventh international conference on Information and knowledge management, November 04-09, 2002, McLean, Virginia, USA
	Martin Ester , Hans-Peter Kriegel , Matthias Schubert, Web site mining: a new way to spot competitors, customers and suppliers in the world wide web, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada
	Rong She , Fei Chen , Ke Wang , Martin Ester , Jennifer L. Gardy , Fiona S. L. Brinkman, Frequent-subsequence-based prediction of outer membrane proteins, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C.
	John F. Roddick , Myra Spiliopoulou, A Survey of Temporal Knowledge Discovery Paradigms and Methods, IEEE Transactions on Knowledge and Data Engineering, v.14 n.4, p.750-767, July 2002
	Claire Grover , Ben Hachey , Ian Hughson , Chris Korycinski, Automatic summarisation of legal documents, Proceedings of the 9th international conference on Artificial intelligence and law, June 24-28, 2003, Scotland, United Kingdom
	D. B. Skillicorn, Parallel frequent set counting, Parallel Computing, v.28 n.5, p.815-825, May 2002
	Claire Grover , Ben Hachey , Chris Korycinski, Summarising legal texts: sentential tense and argumentative roles, Proceedings of the HLT-NAACL 03 on Text summarization workshop, p.33-40, May 31, 2003
	Xiaonan Li , Sigurdur Olafsson, Discovering Dispatching Rules Using Data Mining, Journal of Scheduling, v.8 n.6, p.515-527, December 2005
	Gianluigi Greco , Antonella Guzzo , Luigi Pontieri, Mining taxonomies of process models, Data & Knowledge Engineering, v.67 n.1, p.74-102, October, 2008
	Themis P. Exarchos , Markos G. Tsipouras , Costas Papaloukas , Dimitrios I. Fotiadis, A two-stage methodology for sequence classification based on sequential pattern mining and optimization, Data & Knowledge Engineering, v.66 n.3, p.467-487, September, 2008
	Jianyong Wang , George Karypis, On Mining Instance-Centric Classification Rules, IEEE Transactions on Knowledge and Data Engineering, v.18 n.11, p.1497-1511, November 2006
	Vincent S. Tseng , Chao-Hui Lee, Effective temporal data classification by integrating sequential pattern mining and probabilistic induction, Expert Systems with Applications: An International Journal, v.36 n.5, p.9524-9532, July, 2009
	Ding-Ying Chiu , Yi-Hung Wu , Arbee L. Chen, Efficient frequent sequence mining by a dynamic strategy switching algorithm, The VLDB Journal — The International Journal on Very Large Data Bases, v.18 n.1, p.303-327, January 2009