English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 94188/124659 (76%)
Visitors : 29656747      Online Users : 436
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/113614
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/113614

    Title: The Optimal Estimation of Fuzziness Parameter in Fuzzy C-Means Algorithm
    Authors: 郭訓志
    Kuo, Hsun-Chih
    Lin, Yu-Jau
    Contributors: 統計系
    Keywords: Fuzzy c-means;Xie-Beni index;Simulated annealing;Markov chain
    Date: 2017-06
    Issue Date: 2017-10-16 12:08:18 (UTC+8)
    Abstract: The fuzziness parameter m is an extra parameter that facilitates the iterative formulas of Fuzzy c-means (FCM). However, the parameter m, commonly set to be 2.0, is an important factor that effects the effectiveness of FCM. In literatures, the statistical study of m is so far not available. Viewing m as a random variable, we propose a novel idea to optimize the fuzziness parameter m. For the model selection, a modified cluster validity index is defined as the optimal function of m and improve the effectiveness of FCM. Then the simulated annealing algorithm is applied to approximate its estimate.
    Relation: Lecture Notes in Artificial Intelligence, Vol.LNAI, No.10313
    Data Type: article
    DOI 連結: https://doi.org/10.1007/978-3-319-60837-2_45
    DOI: 10.1007/978-3-319-60837-2_45
    Appears in Collections:[統計學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    2_45.pdf353KbAdobe PDF289View/Open

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback