Efficient Data Mining for Maximal Frequent Subtrees

	This Article
	Subscribers, please Login Purchase article: $19 PDF IEEE Xplore Subscribers RSS feed
	Share
	Email this Article to a friend
	Bibliographic References
	ASCII Text BibTex RefWorks Procite/RefMan/Endnote
	Add to:
	Digg Furl Spurl Blink Simpy Google Del.icio.us Y!MyWeb
	Search
	Similar Articles Articles by Yongqiao Xiao Articles by Jenq-Foung Yao Articles by Zhigang Li Articles by Margaret H. Dunham

Third IEEE International Conference on Data Mining (ICDM'03)

Melbourne, Florida

November 19-November 22

ISBN: 0-7695-1978-4

	ASCII Text	x
	Yongqiao Xiao, Jenq-Foung Yao, Zhigang Li, Margaret H. Dunham, "Efficient Data Mining for Maximal Frequent Subtrees," Data Mining, IEEE International Conference on, pp. 379, Third IEEE International Conference on Data Mining (ICDM'03), 2003.

	BibTex	x
	@article{ 10.1109/ICDM.2003.1250943, author = {Yongqiao Xiao and Jenq-Foung Yao and Zhigang Li and Margaret H. Dunham}, title = {Efficient Data Mining for Maximal Frequent Subtrees}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1978-4}, pages = {379}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250943}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Efficient Data Mining for Maximal Frequent Subtrees SN - 0-7695-1978-4 SP EP A1 - Yongqiao Xiao, A1 - Jenq-Foung Yao, A1 - Zhigang Li, A1 - Margaret H. Dunham, PY - 2003 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Yongqiao Xiao, Georgia College and State University, Milledgeville, GA

Jenq-Foung Yao, Georgia College and State University, Milledgeville, GA

Zhigang Li, Southern Methodist University, Dallas, TX

Margaret H. Dunham, Southern Methodist University, Dallas, TX

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250943

A new type of tree mining is defined in this paper, which uncovers maximal frequent induced subtrees from a database of unordered labeled trees. A novel algorithm, PathJoin, is proposed. The algorithm uses a compact data structure, FST-Forest, which compresses the trees and still keeps the original tree structure. PathJoin generates candidate subtrees by joining the frequent paths in FST-Forest. Such candidate subtree generation is localized and thus substantially reduces the number of candidate subtrees. Experiments with synthetic data sets show that the algorithm is effective and efficient.

Citation:

Yongqiao Xiao, Jenq-Foung Yao, Zhigang Li, Margaret H. Dunham, "Efficient Data Mining for Maximal Frequent Subtrees," icdm, pp.379, Third IEEE International Conference on Data Mining (ICDM'03), 2003

Peer Review Notice | Give Us Feedback

Usage of this product signifies your acceptance of the Terms of Use.