A new algorithm for maintaining closed frequent itemsets in data streams by incremental updates

Hua-Fu Li, Chin-Chuan Ho, Fang-Fei Kuo, Suh-Yin Lee

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

19 Scopus citations

Abstract

Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed algorithm not only attain highly accurate mining results, but also run significant faster and consume less memory than existing algorithm Moment for mining closed frequent itemsets over recent data streams.
Original languageEnglish
Title of host publicationICDM 2006: SIXTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, WORKSHOPS
PublisherIEEE
Pages672-+
ISBN (Print)978-0-7695-2702-4
StatePublished - 2006
Event6th IEEE International Conference on Data Mining - Hong Kong, China
Duration: 18 Dec 200622 Dec 2006

Conference

Conference6th IEEE International Conference on Data Mining
CountryChina
CityHong Kong
Period18/12/0622/12/06

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