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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/99421


    Title: Incrementally Mining Temporal Patterns in Interval-based Databases
    Authors: Chen, Yi-Cheng;Weng, Julia Tzu-Ya;Wang, Jun-Zhe;Chou, Chien-Li;Huang, Jiun-Long;Lee, Suh-Yin
    Contributors: 資訊工程學系暨研究所
    Keywords: dynamic representation;incremental mining;interval-based pattern;sequential pattern mining
    Date: 2014-11-01
    Issue Date: 2014-10-30 17:51:02 (UTC+8)
    Publisher: IEEE
    Abstract: In several applications, sequence databases generally update incrementally with time. Obviously, it is impractical and inefficient to re-mine sequential patterns from scratch every time a number of new sequences are added into the database. Some recent studies have focused on mining sequential patterns in an incremental manner; however, most of them only considered patterns extracted from time point-based data. In this paper, we proposed an efficient algorithm, Inc_TPMiner, to incrementally mine sequential patterns from interval-based data. We also employ some optimization techniques to reduce the search space effectively. The experimental results indicate that Inc_TPMiner is efficient in execution time and possesses scalability. Finally, we show the practicability of incremental mining of interval-based sequential patterns on real datasets.
    Relation: The 2014 International Conference on Data Science and Advanced Analytics
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

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