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International Conference on Rough Sets and Knowledge Technology

RSKT 2007: Rough Sets and Knowledge Technology pp 292–299Cite as

Rough Clustering and Regression Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4481))

Abstract

Since Pawlak introduced rough set theory in 1982 [1] it has gained increasing attention. Recently several rough clustering algorithms have been suggested and successfully applied to real data. Switching regression is closely related to clustering. The main difference is that the distance of the data objects to regression functions has to be minimized in contrast to the minimization of the distance of the data objects to cluster representatives in k-means and k-medoids. Therefore we will introduce rough switching regression algorithms which utilizes the concepts of rough clustering algorithms as introduced by Lingras at al. [2] and Peters [3].

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Authors and Affiliations

  1. Munich University of Applied Sciences, Faculty of Computer Science and Mathematics, Lothstrasse34, 80335 Munich, Germany

    Georg Peters

Authors
  1. Georg Peters
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Editor information

JingTao Yao Pawan Lingras Wei-Zhi Wu Marcin Szczuka Nick J. Cercone Dominik Ślȩzak

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© 2007 Springer Berlin Heidelberg

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Peters, G. (2007). Rough Clustering and Regression Analysis. In: Yao, J., Lingras, P., Wu, WZ., Szczuka, M., Cercone, N.J., Ślȩzak, D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science(), vol 4481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72458-2_36

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  • DOI: https://doi.org/10.1007/978-3-540-72458-2_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72457-5

  • Online ISBN: 978-3-540-72458-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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