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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)

SSPR /SPR 2012: Structural, Syntactic, and Statistical Pattern Recognition pp 51–59Cite as

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  2. Structural, Syntactic, and Statistical Pattern Recognition
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Mode Seeking Clustering by KNN and Mean Shift Evaluated

Mode Seeking Clustering by KNN and Mean Shift Evaluated

  • Robert P. W. Duin24,
  • Ana L. N. Fred25,
  • Marco Loog24 &
  • …
  • Elżbieta Pękalska26 
  • Conference paper
  • 3650 Accesses

  • 9 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7626)

Abstract

Clustering by mode seeking is most popular using the mean shift algorithm. A less well known alternative with different properties on the computational complexity is kNN mode seeking, based on the nearest neighbor rule instead of the Parzen kernel density estimator. It is faster and allows for much higher dimensionalities. We compare the performances of both procedures using a number of labeled datasets. The retrieved clusters are compared with the given class labels. In addition, the properties of the procedures are investigated for prototype selection.

It is shown that kNN mode seeking is well performing and is feasible for large scale problems with hundreds of dimensions and up to a hundred thousand data points. The mean shift algorithm may perform better than kNN mode seeking for smaller dataset sizes.

Keywords

  • Neighborhood Size
  • Cluster Procedure
  • Neighbor Rule
  • Shift Algorithm
  • Prototype Selection

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Pattern Recognition Laboratory, Delft University of Technology, The Netherlands

    Robert P. W. Duin & Marco Loog

  2. Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST - Technical University of Lisbon), Portugal

    Ana L. N. Fred

  3. School of Computer Science, University of Manchester, United Kingdom

    Elżbieta Pękalska

Authors
  1. Robert P. W. Duin
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  2. Ana L. N. Fred
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  3. Marco Loog
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  4. Elżbieta Pękalska
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Auckland, Private Bag 92019, 1142, Auckland, New Zealand

    Georgy Gimel’farb

  2. Department of Computer Science, University of York, Deramore Lane, YO10 5GH, York, UK

    Edwin Hancock

  3. Institute of Media and Information Technology, Chiba University, Yayoi-cho 1-33, 263-8522, Inage-ku, Chiba, Japan

    Atsushi Imiya

  4. Technische Universität/Fraunhofer IGD, Fraunhoferstraße 5, 64283, Darmstadt, Germany

    Arjan Kuijper

  5. Graduate School of Information Science and Technology, Hokkaido University, 060-0814, Sapporo, Japan

    Mineichi Kudo

  6. Graduate School of Engineering, Tohoku University, 6-6-05 Aoba, Aramaki, Aoba-ku, 980-8579, Sendai, Miyagi, Japan

    Shinichiro Omachi

  7. Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Terry Windeatt

  8. C&C Innovation Research Laboratories, NEC Corporation, 8916-47 Takayama-cho, Ikoma-Shi, Nara, Japan

    Keiji Yamada

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Duin, R.P.W., Fred, A.L.N., Loog, M., Pękalska, E. (2012). Mode Seeking Clustering by KNN and Mean Shift Evaluated. In: Gimel’farb, G., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2012. Lecture Notes in Computer Science, vol 7626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34166-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-34166-3_6

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