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    Fuzzy C-means based on automated variable feature weighting

    Nazari, Mousa; Shanbehzadeh, Jamshid; Sarrafzadeh, Hossein

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    Fuzzy C-means based on Automated Variable-IMECS2013_pp25-29.pdf (570.9Kb)
    Date
    2013
    Citation:
    Nazari, M., Shanbehzadeh, J., and Sarrafzadeh, A. (2013). Fuzzy C-means based on automated variable feature weighting. Proceedings of the International MultiConference of Engineers and Computer Scientists, 2013(Ed.), Hong Kong,1. 25-29.
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/2731
    Abstract
    Fuzzy C-means (FCM) is a powerful clustering algorithm and has been introduced to overcome the crisp definition of similarity and clusters. FCM ignores the importance of features in the clustering process. This affects its authenticity and accuracy. We can overcome this problem by appropriately assigning weights to features according to their clustering importance. This paper, proposes an improved FCM algorithm based on the method proposed by Huang by automated feature weighting. The simulation results on several UCI databases show that the proposed algorithm exhibits better performance than FCM.
    Keywords:
    fuzzy clustering, fuzzy C-means, feature weighting, weighted fuzzy C-means
    ANZSRC Field of Research:
    080106 Image Processing
    Copyright Holder:
    Newswood Ltd.

    Copyright Notice:
    All rights reserved
    Available Online at:
    http://www.iaeng.org/IMECS2013/
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use. These documents or images may be used for research or private study purposes. Whether they can be used for any other purpose depends upon the Copyright Notice above. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
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    • Computing Conference Papers [149]

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