Fast and memory efficient mining of high utility itemsets in data streams

Hua Fu Li, Hsin Yun Huang, Yi Cheng Chen, Yu Jiun Liu, Suh Yin Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

70 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 8th IEEE International Conference on Data Mining, ICDM 2008
Pages881-886
Number of pages6
DOIs
StatePublished - 2008
Event8th IEEE International Conference on Data Mining, ICDM 2008 - Pisa, Italy
Duration: 15 Dec 200819 Dec 2008

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference8th IEEE International Conference on Data Mining, ICDM 2008
Country/TerritoryItaly
CityPisa
Period15/12/0819/12/08

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