Efficiently Mining Frequent Closed Partial Orders

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http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.57

Fifth IEEE International Conference o ...

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	ASCII Text	x
	Jian Pei, Jian Liu, Haixun Wang, Ke Wang, Philip S. Yu, Jianyong Wang, "Efficiently Mining Frequent Closed Partial Orders," Data Mining, IEEE International Conference on, pp. 753-756, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005.

	BibTex	x
	@article{ 10.1109/ICDM.2005.57, author = {Jian Pei and Jian Liu and Haixun Wang and Ke Wang and Philip S. Yu and Jianyong Wang}, title = {Efficiently Mining Frequent Closed Partial Orders}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2005}, issn = {1550-4786}, pages = {753-756}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.57}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Efficiently Mining Frequent Closed Partial Orders SN - 1550-4786 SP753 EP756 A1 - Jian Pei, A1 - Jian Liu, A1 - Haixun Wang, A1 - Ke Wang, A1 - Philip S. Yu, A1 - Jianyong Wang, PY - 2005 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Jian Pei, Simon Fraser University

Jian Liu, State University of New York at Buffalo

Haixun Wang, IBM T.J. Watson Research Center

Ke Wang, Simon Fraser University

Philip S. Yu, IBM T.J. Watson Research Center

Jianyong Wang, Tsinghua University

Mining ordering information from sequence data is an important data mining task. Sequential pattern mining [1] can be regarded as mining frequent segments of total orders from sequence data. However, sequential patterns are often insufficient to concisely capture the general ordering information.

Citation:

Jian Pei, Jian Liu, Haixun Wang, Ke Wang, Philip S. Yu, Jianyong Wang, "Efficiently Mining Frequent Closed Partial Orders," icdm, pp.753-756, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005

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