Browsing by Subject "sequential pattern mining"

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    Mining Uncertain Sequential Patterns in Iterative MapReduce
    (Springer, 2015) Ge, Jiaqi; Xia, Yuni; Wang, Jian; Department of Computer and Information Science, School of Science
    This paper proposes a sequential pattern mining (SPM) algorithm in large scale uncertain databases. Uncertain sequence databases are widely used to model inaccurate or imprecise timestamped data in many real applications, where traditional SPM algorithms are inapplicable because of data uncertainty and scalability. In this paper, we develop an efficient approach to manage data uncertainty in SPM and design an iterative MapReduce framework to execute the uncertain SPM algorithm in parallel. We conduct extensive experiments in both synthetic and real uncertain datasets. And the experimental results prove that our algorithm is efficient and scalable.
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    Towards Efficient Sequential Pattern Mining in Temporal Uncertain Databases
    (Springer, 2015) Ge, Jiaqi; Xia, Yuni; Wang, Jian; Department of Computer and Information Science, School of Science
    Uncertain sequence databases are widely used to model data with inaccurate or imprecise timestamps in many real world applications. In this paper, we use uniform distributions to model uncertain timestamps and adopt possible world semantics to interpret temporal uncertain database. We design an incremental approach to manage temporal uncertainty efficiently, which is integrated into the classic pattern-growth SPM algorithm to mine uncertain sequential patterns. Extensive experiments prove that our algorithm performs well in both efficiency and scalability.