Efficient Mining of High Branching Factor Attribute Trees

	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 Alexandre Termier Articles by Marie-Christine Rousset Articles by Michèle Sebag Articles by Kouzou Ohara Articles by Takashi Washio Articles by Hiroshi Motoda

Fifth IEEE International Conference on Data Mining (ICDM'05)

Houston, Texas

November 27-November 30

ISBN: 0-7695-2278-5

	ASCII Text	x
	Alexandre Termier, Marie-Christine Rousset, Michèle Sebag, Kouzou Ohara, Takashi Washio, Hiroshi Motoda, "Efficient Mining of High Branching Factor Attribute Trees," Data Mining, IEEE International Conference on, pp. 785-788, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005.

	BibTex	x
	@article{ 10.1109/ICDM.2005.55, author = {Alexandre Termier and Marie-Christine Rousset and Michèle Sebag and Kouzou Ohara and Takashi Washio and Hiroshi Motoda}, title = {Efficient Mining of High Branching Factor Attribute Trees}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2005}, issn = {1550-4786}, pages = {785-788}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.55}, 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 Mining of High Branching Factor Attribute Trees SN - 1550-4786 SP785 EP788 A1 - Alexandre Termier, A1 - Marie-Christine Rousset, A1 - Michèle Sebag, A1 - Kouzou Ohara, A1 - Takashi Washio, A1 - Hiroshi Motoda, PY - 2005 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Alexandre Termier, Osaka University

Marie-Christine Rousset, CNRS, Université Paris-Sud and INRIA

Michèle Sebag, CNRS, Université Paris-Sud and INRIA

Kouzou Ohara, Osaka University

Takashi Washio, Osaka University

Hiroshi Motoda, Osaka University

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.55

In this paper, we present a new tree mining algorithm, DRYADEPARENT, based on the hooking principle first introduced in DRYADE [9]. In the experiments, we demonstrate that the branching factor and depth of the frequent patterns to find are key factor of complexity for tree mining algorithms. We show that DRYADEPARENT outperforms the current fastest algorithm, CMTreeMiner, by orders of magnitude on datasets where the frequent patterns have a high branching factor.

Citation:

Alexandre Termier, Marie-Christine Rousset, Michèle Sebag, Kouzou Ohara, Takashi Washio, Hiroshi Motoda, "Efficient Mining of High Branching Factor Attribute Trees," icdm, pp.785-788, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005

Peer Review Notice | Give Us Feedback

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