Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams

被引：47

作者：

Li, Hua-Fu ^{[1
]}

Huang, Hsin-Yun ^{[2
]}

Chen, Yi-Cheng ^{[2
]}

Liu, Yu-Jiun ^{[2
]}

Lee, Suh-Yin ^{[2
]}

机构：

[1] Kainan Univ, Dept Comp Sci, Tao Yuan, Taiwan

[2] Natl Chiao Tung Univ, Dept Comp Sci, Hsinchu, Taiwan

来源：

ICDM 2008: EIGHTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/ICDM.2008.107

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

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.

引用

页码：881 / +

页数：2

共 13 条

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[2]

Chan R, 2003, P 3 IEEE INT C DAT M

[3]

Moment: Maintaining closed frequent itemsets over a stream sliding window [J].