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    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/78595

    題名: Incremental Mining of Closed Sequential Patterns in Multiple Data Streams
    作者: Yang, Shih-yang;Chao, Ching-ming;Chen, Po-zung;Sun, Chu-hao
    貢獻者: 淡江大學資訊工程學系
    關鍵詞: Multiple Data Streams;Data Stream Mining;Sequential Pattern Mining;Incremental Mining
    日期: 2011-05
    上傳時間: 2012-10-17 10:53:34 (UTC+8)
    出版者: Oulu: Academy Publisher
    摘要: 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.
    關聯: Journal of Networks 6(5), pp.728-735
    DOI: 10.4304/jnw.6.5.728-735
    顯示於類別:[資訊工程學系暨研究所] 期刊論文


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