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Gradient-Based FCM and a Neural Network for Clustering of Incomplete Data

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3610))

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Abstract

Clustering of incomplete data using a neural network and the Gradient-Based Fuzzy c-Means (GBFCM) is proposed in this paper. The proposed algorithm is applied to the Iris data to evaluate its performance. When compared with the existing Optimal Completion Strategy FCM (OCSFCM), the proposed algorithm shows 18%-20% improvement of performance over the OCSFCM.

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Author information

Authors and Affiliations

  1. Intelligent Computing Research Lab., Dept. of Information Engineering, Myong Ji University, Korea

    Dong-Chul Park

Authors
  1. Dong-Chul Park
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Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Park, DC. (2005). Gradient-Based FCM and a Neural Network for Clustering of Incomplete Data. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_166

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  • DOI: https://doi.org/10.1007/11539087_166

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28323-2

  • Online ISBN: 978-3-540-31853-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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