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    题名: TRADE-OFF BETWEEN COMPUTATION TIME AND NUMBER OF RULES FOR FUZZY MINING FROM QUANTITATIVE DATA
    作者: Kuo, Chan-Sheng
    贡献者: 資管系
    日期: 2001
    上传时间: 2015-12-10 18:09:00 (UTC+8)
    摘要: Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most conventional data-mining algorithms identify the relationships among transactions using binary values. Transactions with quantitative values are however commonly seen in real-world applications. We proposed a fuzzy mining algorithm by which each attribute used only the linguistic term with the maximum cardinality int he mining process. The number of items was thus the same as that of the original attributes, making the processing time reduced. The fuzzy association rules derived in this way are not complete. This paper thus modifies it and proposes a new fuzzy data-mining algorithm for extrating interesting knowledge from transactions stored as quantitative values. The proposed algorithm can derive a more complete set of rules but with more computation time than the method proposed. Trade-off thus exists between the computation time and the completeness of rules. Choosing an appropriate learning method thus depends on the requirement of the application domains.
    關聯: International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems , Volume 09, Issue 05, October 2001
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1142/S0218488501001071
    DOI: 10.1142/S0218488501001071
    显示于类别:[資訊管理學系] 期刊論文

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