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Clustering High-Dimensional Data

In: Machine Learning for Data Science Handbook

Author

Listed:
  • Michael E. Houle

    (National Institute of Informatics)

  • Marie Kiermeier

    (Ludwig-Maximilians-Universität München)

  • Arthur Zimek

    (University of Southern Denmark, Department of Mathematics and Computer Science)

Abstract

Clustering algorithms have been adapted or specifically designed for high-dimensional data where many attributes might be just noise such that patterns can be identified only in appropriate combinations of attributes and would be obfuscated by noise otherwise. In this chapter, we give an overview of the basic strategies and techniques used for these specialized algorithms along with pointers to example methods.

Suggested Citation

  • Michael E. Houle & Marie Kiermeier & Arthur Zimek, 2023. "Clustering High-Dimensional Data," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 219-237, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-24628-9_11
    DOI: 10.1007/978-3-031-24628-9_11
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