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A New Methodology to Compare Algorithms

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Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents (IDEAL 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1983))

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Abstract

In the context of unsupervised clustering, lots of different algorithms have been proposed. Most of them consist in optimizing an objective function using a search strategy. We present here a new methodology for studying and comparing the performances of the objective functions and search strategies employed.

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References

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Author information

Authors and Affiliations

  1. UMR CNRS 5823 - LASS - Bât 101, Université Lyon 1, 69622, Villeurbanne, France

    C. Robardet & F. Feschet

Authors
  1. C. Robardet
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  2. F. Feschet
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Editor information

Editors and Affiliations

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

    Kwong Sak Leung  & Lai-Wan Chan  & 

  2. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong

    Helen Meng

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© 2000 Springer-Verlag Berlin Heidelberg

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Robardet, C., Feschet, F. (2000). A New Methodology to Compare Algorithms. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_82

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  • DOI: https://doi.org/10.1007/3-540-44491-2_82

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41450-6

  • Online ISBN: 978-3-540-44491-6

  • eBook Packages: Springer Book Archive

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