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 proceedingChapterpeer-review

55 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 publicationICDM 2008: EIGHTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS
PublisherIEEE
Pages88-+
ISBN (Electronic)978-0-7695-3502-9
ISBN (Print)978-0-7695-3502-9
DOIs
StatePublished - 2008
Event8th IEEE International Conference on Data Mining - Pisa, Italy
Duration: 15 Dec 200819 Dec 2008

Publication series

NameIEEE International Conference on Data Mining
PublisherIEEE
ISSN (Print)1550-4786
ISSN (Electronic)1550-4786

Conference

Conference8th IEEE International Conference on Data Mining
CountryItaly
CityPisa
Period15/12/0819/12/08

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