Incremental mining of sequential patterns over a stream sliding window

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

Research output: Chapter in Book/Report/Conference proceedingChapter

38 Scopus citations

Abstract

Incremental mining of sequential patterns from data streams is one of the most challenging problems in mining data streams. However, previous work of mining sequential patterns from data streams is almost focused on mining of patterns from stream of item-sequences, not stream of itemset-sequences. In this paper, we propose an efficient single-pass algorithm, called IncSPAM, to maintain the set of sequential patterns from itemset-sequence 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 IncSPAM algorithm is efficient for mining sequential patterns over data streams.
Original languageEnglish
Title of host publicationICDM 2006: SIXTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, WORKSHOPS
PublisherIEEE
Pages677-+
ISBN (Print)978-0-7695-2702-4
DOIs
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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