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Comparing Fuzzy Data Sets by Means of Graph Matching Technique

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Book cover Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003 (ICANN 2003, ICONIP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2714))

Abstract

In several applications it is necessary to compare two or more data sets. In this paper we describe a new technique to compare two data partitions of two different data sets with a quite similar structure as frequently occurs in defect detection. The comparison is obtained dividing each data set in partitions by means of a supervised fuzzy clustering algorithm and associating an undirected complete weighted graph structure to these partitions. Then, a graph matching operation returns an estimation of the level of similarity between the data sets.

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Author information

Authors and Affiliations

  1. D.E.E.-Electro-technology and Electronics Department, Politecnico di Bari, Via E. Orabona, 4, I-70125, Bari, Italy

    Giuseppe Acciani, Girolamo Fornarelli & Luciano Liturri

Authors
  1. Giuseppe Acciani
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  2. Girolamo Fornarelli
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  3. Luciano Liturri
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Editor information

Editors and Affiliations

  1. Bogazici University, Bebek, 34342, Istanbul, Turkey

    Okyay Kaynak  & Ethem Alpaydin  & 

  2. Laboratory of Computer and Information Science, Helsinki University of Technology, P.O.B. 5400, 02015, Finland

    Erkki Oja

  3. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Lei Xu

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© 2003 Springer-Verlag Berlin Heidelberg

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Acciani, G., Fornarelli, G., Liturri, L. (2003). Comparing Fuzzy Data Sets by Means of Graph Matching Technique. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_44

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  • DOI: https://doi.org/10.1007/3-540-44989-2_44

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40408-8

  • Online ISBN: 978-3-540-44989-8

  • eBook Packages: Springer Book Archive

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