標題: Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams
作者: Li, Hua-Fu
Huang, Hsin-Yun
Chen, Yi-Cheng
Liu, Yu-Jiun
Lee, Suh-Yin
資訊工程學系
Department of Computer Science
公開日期: 2008
摘要: Efficient mining of high utility itemsets has become one of the most interesting data mining tasks with broad applications. In this paper, we proposed two efficient one-pass algorithms, MHUI-BIT and MHUI-TID, for mining high utility itemsets from data streams within a transaction-sensitive sliding window. Two effective representations of item information and an extended lexicographical tree-based summary data structure are developed to improve the efficiency of mining high utility itemsets. Experimental results show that the proposed algorithms outperform than the existing algorithms for mining high utility itemsets from data streams.
URI: http://dx.doi.org/10.1109/ICDM.2008.107
http://hdl.handle.net/11536/135084
ISBN: 978-0-7695-3502-9
ISSN: 1550-4786
DOI: 10.1109/ICDM.2008.107
期刊: ICDM 2008: EIGHTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS
起始頁: 881
結束頁: +
Appears in Collections:Conferences Paper