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研究生: 陳弘庭
Chen, Hung-Ting
論文名稱: 模糊分群方法、語意變數、分群群數關係之研究─以市場區隔為例
指導教授: 王泰裕
Wang, Tai-Yue
學位類別: 碩士
Master
系所名稱: 管理學院 - 工業管理科學系
Department of Industrial Management Science
論文出版年: 2003
畢業學年度: 91
語文別: 中文
論文頁數: 90
中文關鍵詞: 模糊績效指標模糊分群方法模糊語意變數
外文關鍵詞: fuzzy clustering methods, fuzzy clustering index
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  • 長期以來,各種不同的分群方法被廣泛運用在管理的領域上,包括:市場區隔、顧客分群、產品分類、人員配置與評估、機器配置等等。透過對所收集的資料作分群的工作,可以縮小評估決策的範圍,也就是說可視為對問題解答空間的分割,減少在解答空間的搜尋時間。對於管理活動而言,常會遭遇到一些語意模糊不清、似懂非懂的困擾,而無法以精確的數字來表達,因此本研究欲透過模糊語意變數來提供相關訊息。透過問卷的發放,得知問卷受訪者的態度傾向,再經由模糊分群方法將資料作分群,利用模糊績效指標可將分群結果的好壞作績效評估。因此對於模糊分群方法、分群數與模糊語意變數三因子的組合會有怎樣的分群結果,需要選擇一適合的模糊績效指標來衡量,本研究架構一個分群結果比較的模式,此一模式不僅可以做不同的模糊分群方法在不同的分群數與模糊語意變數之比較,對於不同模糊績效指標的效果衡量作比較,企圖從資料的分析中,尋找出最佳的模糊分群方法、分群數、模糊語意變數與模糊績效指標,以作為問卷設計者在設計問卷前的意見提供與在市場區隔中之應用分析。因此,本研究透過模擬驗證的方式,將分群結果透過指標的計算,代入三因子實驗設計中,對三個因子間個別或者交互作用的顯著性作深入探討,分別對顯著的重要因子或交互作用做最佳化因子水準組合配適。

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    摘要.....................................................................i 誌謝.....................................................................ii 目錄.....................................................................iii 表目錄...................................................................vi 圖目錄...................................................................vii 第一章 緒論..........................................................1 第一節 研究動機.......................................................1 第二節 研究目的.......................................................3 第三節 研究範圍......................................................4 第四節 研究架構......................................................5 第五節 論文大綱......................................................5 第二章 文獻探討......................................................7 第一節 模糊理論.......................................................7 第二節 模糊集群分析..................................................14 第三節 模糊測度的績效指標............................................26 第四節 模糊分群方法與指標所建構的模式................................29 第五節 小結..........................................................33 第三章 最佳語意項數、模糊分群方法與分群群數的選擇....................34 第一節 衡量分群績效指標的選擇.........................................36 第二節 語意變數項數與型態的選擇.......................................37 第三節 模糊分群方法的選擇............................................39 第四節 三因子實驗設計................................................41 第五節 最佳化因子水準組合............................................45 第六節 小結..........................................................47 第四章 模擬驗證......................................................48 第一節 資料來源......................................................50 第二節 模糊分群結果之探討............................................54 第三節 實驗設計分析..................................................63 第四節 最佳化因子水準組合............................................69 第五節 小結..........................................................73 第五章 結論與建議....................................................76 第一節 研究結論.......................................................76 第二節 後續研究建議...................................................79 參考文獻.................................................................81 中文部分...............................................................81 英文部分...............................................................82 附錄一...................................................................85 附錄二...................................................................87 簡歷.....................................................................90

    中文部分:
    林信成、彭啟峰,“OH!Fuzzy模糊理論剖析”,第三波發行,民國83年。
    林靈宏,“消費者行為學”,五南圖書出版公司,民國83年。
    鄭伯壎,“消費者心理學”,大洋出版社,民國77年。
    黎正中,“實驗設計與分析”,高立圖書有限公司,民國87年。
    謝依真,“不同分群方法與不同資料來源之比較”,國立成功大學工業管理研究所,民國90年六月。
    張文豪,“李克特式量表、分群方法與分群群數關係之研究”,國立成功大學工業管理研究所,民國91年六月。

    英文部分:
    Backer, E. and Jain, A.K. “A clustering performance measure based on fuzzy set decomposition,” IEEE Trans. Pattern analy. Mach. Intell. , Vol. 3, No. 1, pp.66-75, 1981.

    Bezdek, J.C., “Cluster Validity with Fuzzy Sets,” Journal of Cybernetics, Vol. 3, pp.58-73, 1974.

    Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York, 1981.

    Bezdek, J.C., “Mathematical models for systematics and taxonomy,” Proc. 8th Internat. Conf. Numerical Taxonomy, San Francisco, pp.143-166, 1975.

    Bezdek, J.C. and Pal, N.R. “ On cluster Validity for the Fuzzy c-Means Model,” IEEE Transactions on Fuzzy Systems, Vol. 3, pp.370-379, 1995.

    Bezdek, J.C. and Pal, N.R. “Correction On cluster Validity for the Fuzzy c-Means Model,” IEEE Transactions on Fuzzy Systems, Vol. 5, pp.152-153, 1997.

    Buckley, J.J. and Hayashi, Y. “Application of fuzzy chaos to fuzzy simulation,” Fuzzy Sets and Systems, Vol. 99, pp. 151-157, 1998.

    Dammert, P.B.G. and Askne, J.I.H. “Unsupervised segmentation of multitemporal interferometric SAR images,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No.5, pp.2259-2271, 1999.

    Dave, R.N. “Generalized fuzzy c-shells clustering and detection of circular and elliptical boundaries,” Pattern Recognition, Vol. 25, No. 7, pp.713-721, 1992.

    Davis, D.L. and Bouldin, D.W. “A cluster separation easure,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 1, No. 2, pp.224-227, 1979.

    Devillez, A., Billaudel, P. and Lecolier, G.V. “A fuzzy hybrid hierarchical clustering method with a new criterion able to optimal partition,” Fuzzy Sets and Systems, Vol. 128, pp.323-338, 2002.

    Dunn, J.C. “Well-Separated Cluster and the Optimal Fuzzy Partitions,” Journal of Cybernetic, Vol. 4, pp.95-104, 1974.

    Fishwick, P.A. “Fuzzy simulation: specifying and entifying qualitative models,” Int. J. General systems, Vol. 19, pp.295-316, 1991.

    Fukuyama, Y. and Sugeno, M. “A new method of choosing the number of clusters for the Fuzzy C-means method,” Proc. 5th Fuzzy Systems Symposium, pp.247-250, 1989.

    Jaynes, E. T. “Information theory and statistical mechanics,” Phys. Rev., Vol. 106, pp620-630, pp.171-190 , 1957.

    Li, R. and Mukaidono, M. “Gaussian clustering method based on maximum fuzzy entropy interpretation,” Fuzzy Sets and Systems, Vol. 102, pp.253-258, 1999.

    Liu, M. and Samal, A. “Cluster validation using legacy delineations,” Image and Vision Computing, Vol. 20, pp.459-467, 2002.

    Pedrycz, W. and Vukovich, G. “ Logic-oriented fuzzy clustering,” Pattern Recognition Letters , Vol. 23, pp.1515-1527, 2002.

    Rezaee, M.R., Lelieveldt, B.P.F. and Reiber, J.H.C. “ A new cluster validity index for fuzzy C-mean,” Pattern Recognition Letter, Vol. 19, pp.237-246,1998.

    Stutz, C. and Runkler, T.A. “Classification and prediction of road traffic using application-specific fuzzy clustering,” IEEE Transactions on Fuzzy Systems, Vol. 10, No. 3, pp.297-308, 2002.

    Windham, M.P. “ Cluster validity for fuzzy clustering algorithms,” Fuzzy Sets and Systems , Vol. 5, pp.177-185, 1981.

    Xie, L.X. and Beni, G. “A validity measure for fuzzy clustering,” IEEE Transactions Pattern Anal. Mach. Intell. , Vol. 13, pp.841-847, 1991.

    Yang, H. and Luh, P.B. “A fuzzy optimization-based method for integrated power system scheduling and inter-utility power transaction with uncertainties,” IEEE Transactions on Power Systems, Vol. 12, No. 2, pp756-763, 1997.

    Yang, M.S. “A survey of fuzzy clustering,” Mathematical and computer modeling, Vol. 18, pp.1-16, 1993.

    Zadeh, L.A. “Fuzzy sets,” Information and Control, Vol. 8, No. 3, pp.338-353, 1965.

    Zadeh, L.A. “The concept of a linguistic variable and its application to approximate reasoning,” Information Sciences , Vol. 8, pp.199-249, 1975.

    Zadeh, L.A. “The concept of a linguistic variable and its application to approximate reasoning,” Information Sciences , Vol. 8, pp.301-357 , 1975.

    Zadeh, L.A. “The concept of a linguistic variable and its application to approximate reasoning,” Information Sciences, Vol. 9, pp. 43-80, 1975.

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