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Unsupervised Learning: Clustering

  • Bertrand Clarke
  • Ernest Fokoué
  • Hao Helen Zhang
Chapter
Part of the Springer Series in Statistics book series (SSS)

In contrast to supervised learning, unsupervised learning fits a model to observations assuming there is no dependent random variable, output, or response. That is, a set of input observations is gathered and treated as a set of random variables and analyzed as is. None of the observations is treated differently from the others. An informal way to say this is that there is no Y. For this reason, sometimes classification data that includes the Y as the class is called labeled data but clustering data is called unlabeled. Then, it’s as if the task of clustering is to surmise what variable Y should have been measured (but wasn’t). Another way to think of this is to assume that there are n independent data vectors (X 1, ...,Xp,Y) but that all the Y is are missing, and in fact someone has even hidden the definition of Y.

Keywords

Cluster Center Minimal Span Tree Unsupervised Learn Spectral Cluster Single Linkage 
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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Copyright information

© Springer-Verlag New York 2009

Authors and Affiliations

  • Bertrand Clarke
    • 1
  • Ernest Fokoué
    • 2
  • Hao Helen Zhang
    • 3
  1. 1.University of MiamiMiamiCanada
  2. 2.Department of Science & MathematicsKettering UniversityFlintUSA
  3. 3.Department of StatisticsNorth Carolina State University Program in Statistical GeneticsRaleighUSA

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