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Advances in Self-Organizing Maps and Learning Vector Quantization pp 85–93Cite as

A Neural Gas Based Approximate Spectral Clustering Ensemble

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 428))

Abstract

The neural gas has been successfully used for prototype based clustering approaches. Its topology based quantization effectively aids in approximate spectral clustering (ASC) to define distinct similarity criteria which are optimally selected for the relevant application. To utilize the advantages of ASC by harnessing those criteria derived from different information types, we propose a neural gas based approximate spectral clustering ensemble (NGASCE). The NGASCE obtains a joint decision for accurate partitioning, by a 2-step ensemble approach derived from 1-step graph-based models. We show the outperformance of NGASCE on five datasets from UCI Machine Learning Repository.

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Acknowledgments

This work is funded by TUBITAK Career Integration Grant 112E195. Taşdemir is also funded by FP7 Marie Curie Career Integration Grant IAM4MARS.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Istanbul Technical University, ITU Ayazaga Kampusu, Ayazaga, Istanbul, Turkey

    Yaser Moazzen

  2. Department of Computer Engineering, Antalya International University, Universite Cd. 2, 07190, Dosemealti, Antalya, Turkey

    Kadim Taşdemir

Authors
  1. Yaser Moazzen
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  2. Kadim Taşdemir
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Corresponding authors

Correspondence to Yaser Moazzen or Kadim Taşdemir .

Editor information

Editors and Affiliations

  1. Department of Statistics, Rice University, Houston, Texas, USA

    Erzsébet Merényi

  2. Department of Electrical and Computer En, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA

    Michael J. Mendenhall

  3. Applied Physics, Rice University, Houston, Texas, USA

    Patrick O'Driscoll

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© 2016 Springer International Publishing Switzerland

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Moazzen, Y., Taşdemir, K. (2016). A Neural Gas Based Approximate Spectral Clustering Ensemble. In: Merényi, E., Mendenhall, M., O'Driscoll, P. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-319-28518-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-28518-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28517-7

  • Online ISBN: 978-3-319-28518-4

  • eBook Packages: EngineeringEngineering (R0)

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