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Classification and Clustering for Knowledge Discovery pp 1–13Cite as

The Many Faces of a Kohonen Map A Case Study: SOM-based Clustering for On-Line Fraud Behavior Classification

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 4))

Abstract

The Self-Organizing Map (SOM) is an excellent tool for exploratory data analysis. It projects the input space on prototypes of a low-dimensional regular grid which can be efficiently used to visualize and explore the properties of the data.

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Authors and Affiliations

  1. Telecommunication and Neural Techniques Group, France Telecom Research and Development, FTR&D/TECH/SUSI, 2 Avenue Pierre Marzin, 22307 Lannion cedex, France

    V. Lemaire & F. Clérot

Authors
  1. V. Lemaire
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  2. F. Clérot
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Saman K. Halgamuge Lipo Wang

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Lemaire, V., Clérot, F. The Many Faces of a Kohonen Map A Case Study: SOM-based Clustering for On-Line Fraud Behavior Classification. In: K. Halgamuge, S., Wang, L. (eds) Classification and Clustering for Knowledge Discovery. Studies in Computational Intelligence, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11011620_1

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  • DOI: https://doi.org/10.1007/11011620_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26073-8

  • Online ISBN: 978-3-540-32404-1

  • eBook Packages: EngineeringEngineering (R0)

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