Publications-Periodical Articles

Article View/Open

Publication Export

Google ScholarTM

NCCU Library

Citation Infomation

Related Publications in TAIR

題名 Classification of Tea Quality With Fuzzy Cluster Analysis
作者 鄭宇庭
Cheng, Yu-Ting
關鍵詞 Classification ; Tea Quality ; Fuzzy Cluster Analysis ; Neural Network ; Membership Functions
日期 2001-03
上傳時間 19-Dec-2008 14:53:43 (UTC+8)
摘要 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.
關聯 PanPacfic Management Review 5(1),93-111
資料類型 article
dc.creator (作者) 鄭宇庭zh_TW
dc.creator (作者) Cheng, Yu-Ting-
dc.date (日期) 2001-03en_US
dc.date.accessioned 19-Dec-2008 14:53:43 (UTC+8)-
dc.date.available 19-Dec-2008 14:53:43 (UTC+8)-
dc.date.issued (上傳時間) 19-Dec-2008 14:53:43 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/18184-
dc.description.abstract (摘要) 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.-
dc.format application/en_US
dc.language enen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) PanPacfic Management Review 5(1),93-111en_US
dc.subject (關鍵詞) Classification ; Tea Quality ; Fuzzy Cluster Analysis ; Neural Network ; Membership Functions-
dc.title (題名) Classification of Tea Quality With Fuzzy Cluster Analysisen_US
dc.type (資料類型) articleen