Mining High Utility Itemsets in Large High Dimensional Data

W
WKDD
2008
First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)
Abstract - Mining High Utility Itemsets in Large High Dimensional Data

	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
	Add to:
	Digg Furl Spurl Blink Simpy Google Del.icio.us Y!MyWeb
	Search
	Similar Articles Articles by Guangzhu Yu Articles by Keqing Li Articles by Shihuang Shao

First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)

Adelaide, Australia

January 23-January 24

ISBN: 0-7695-3090-7

	ASCII Text	x
	Guangzhu Yu, Keqing Li, Shihuang Shao, "Mining High Utility Itemsets in Large High Dimensional Data," International Workshop on Knowledge Discovery and Data Mining, pp. 17-20, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008.

	BibTex	x
	@article{ 10.1109/WKDD.2008.64, author = {Guangzhu Yu and Keqing Li and Shihuang Shao}, title = {Mining High Utility Itemsets in Large High Dimensional Data}, journal ={International Workshop on Knowledge Discovery and Data Mining}, volume = {0}, year = {2008}, isbn = {0-7695-3090-7}, pages = {17-20}, doi = {http://doi.ieeecomputersociety.org/10.1109/WKDD.2008.64}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - International Workshop on Knowledge Discovery and Data Mining TI - Mining High Utility Itemsets in Large High Dimensional Data SN - 0-7695-3090-7 SP17 EP20 A1 - Guangzhu Yu, A1 - Keqing Li, A1 - Shihuang Shao, PY - 2008 VL - 0 JA - International Workshop on Knowledge Discovery and Data Mining ER -

Guangzhu Yu

Keqing Li

Shihuang Shao

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WKDD.2008.64

Existing algorithms for utility mining are inadequate on datasets with high dimensions or long patterns. This paper proposes a hybrid method, which is composed of a row enumeration algorithm (i.e., Inter-transaction) and a column enumeration algorithm (i.e., Two-phase), to discover high utility itemsets from two directions: Two-phase seeks short high utility itemsets from the bottom, while Intertransaction seeks long high utility itemsets from the top. In addition, optimization technique is adopted to improve the performance of computing the intersection of transactions. Experiments on synthetic data show that the hybrid method achieves high performance in large high dimensional datasets.

Citation:

Guangzhu Yu, Keqing Li, Shihuang Shao, "Mining High Utility Itemsets in Large High Dimensional Data," wkdd, pp.17-20, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008

Peer Review Notice | Give Us Feedback

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