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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/18184


    Title: Classification of Tea Quality With Fuzzy Cluster Analysis
    Authors: 鄭宇庭
    Cheng, Yu-Ting
    Keywords: Classification;Tea Quality;Fuzzy Cluster Analysis;Neural Network;Membership Functions
    Date: 2001-03
    Issue Date: 2008-12-19 14:53:43 (UTC+8)
    Abstract: This paper aims to improve the fuzzy clustering method by proposing the weighted fuzzy c-means clustering method instead. We emphasize on the concept of factor weight combined with the fuzzy clustering analysis, which has been ignored by most of the conventional fuzzy clustering analysis. Weight of factors and membership functions are obtained from experts via sampling survey. The integrated fuzzy classification procedure is developed and fuzzy inference neural networks based on the statistical model is suggested. Finally, as for an empirical example, we apply the proposed technique to the classification for the 69 Taiwan tea qualities. It exhibits that our proposed integrated fuzzy cluster method demonstrates more efficient and better results than traditional ones did.
    Relation: PanPacfic Management Review 5(1),93-111
    Data Type: article
    Appears in Collections:[Department of Statistics] Periodical Articles

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