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    Mining frequent sequences using itemset-based extension

    118188_9998_PUB-CBS-EEB-MC-47237.pdf (441.1Kb)
    Access Status
    Open access
    Authors
    Ma, Zhixin
    Xu, Yusheng
    Dillon, Tharam S.
    Chen, Xiaoyun
    Date
    2008
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Ma, Zhixin and Xu, Yusheng and Dillon, Tharam S. and Chen, Xiaoyun. 2008. Mining frequent sequences using itemset-based extension, in Craig Douglas and Ping-Kong Alexander Wai (ed), International MultiConference of Engineers and Computer Scientists (IMECS 2008), Mar 19 2008, pp. 591-596.Hong Kong: IAENG
    Source Title
    Proceedings of the international multiconference of engineers and computer scientists (IMECS 2008)
    Source Conference
    International MultiConference of Engineers and Computer Scientists (IMECS 2008)
    ISBN
    9789889867188
    Faculty
    Curtin Business School
    The Centre for Extended Enterprises and Business Intelligence (CEEBI)
    School
    Centre for Extended Enterprises and Business Intelligence
    Remarks

    The link to the International MultiConference of Engineers and Computer Scientists (IMECS 2008) is : http://www.iaeng.org/IMECS2008/

    URI
    http://hdl.handle.net/20.500.11937/9047
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we systematically explore an itemset-based extension approach for generating candidate sequence which contributes to a better and more straightforward search space traversal performance than traditional item-based extension approach. Based on this candidate generation approach, we present FINDER, a novel algorithm for discovering the set of all frequent sequences. FINDER is composed oftwo separated steps. In the first step, all frequent itemsets are discovered and we can get great benefit from existing efficient itemset mining algorithms. In the second step, all frequent sequcnces with at least two frequent itemsets are detected by combining depth-first search and item set-based extension candidate generation together. A vertical bitmap data representation is adopted for rapidly support counting reason. Several pruning strategies are used to reduce the search space and minimize cost of computation. An extensive set ofexperiments demonstrate the effectiveness and the linear scalability of proposed algorithm.

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