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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/78595

    Title: Incremental Mining of Closed Sequential Patterns in Multiple Data Streams
    Authors: Yang, Shih-yang;Chao, Ching-ming;Chen, Po-zung;Sun, Chu-hao
    Contributors: 淡江大學資訊工程學系
    Keywords: Multiple Data Streams;Data Stream Mining;Sequential Pattern Mining;Incremental Mining
    Date: 2011-05
    Issue Date: 2012-10-17 10:53:34 (UTC+8)
    Publisher: Oulu: Academy Publisher
    Abstract: Sequential pattern mining searches for the relative sequence of events, allowing users to make predictions on discovered sequential patterns. Due to drastically advanced information technology over recent years, data have rapidly changed, growth in data amount has exploded and real-time demand is increasing, leading to the data stream environment. Data in this environment cannot be fully stored and ineptitude in traditional mining techniques has led to the emergence of data stream mining technology. Multiple data streams are a branch of the data stream environment. The MILE algorithm cannot preserve previously mined sequential patterns when new data are entered because of the concept of one-time fashion mining. To address this problem, we propose the ICspan algorithm to continue mining sequential patterns through an incremental approach and to acquire a more accurate mining result. In addition, due to the algorithm constraint in closed sequential patterns mining, the generation and records for sequential patterns will be reduced, leading to a decrease of memory usage and to an effective increase of execution efficiency.
    Relation: Journal of Networks 6(5), pp.728-735
    DOI: 10.4304/jnw.6.5.728-735
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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