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Biclustering

  • Antonio Mucherino
  • Petraq J. Papajorgji
  • Panos M. Pardalos
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 34)

Abstract

Clustering techniques aim at partitioning a given set of data into clusters. Chapter 3 presents the basic k-means approach and many variants to the standard algorithm. All these algorithms search for an optimal partition in clusters of a given set of samples. The number of clusters is usually denoted by the symbol k. As previously discussed in Chapter 3, each cluster is usually labeled with an integer number ranging from 0 to k- 1. Once a partition is available for a certain set of samples, the samples can then be sorted by the label of the corresponding cluster in the partition. If a color is then assigned to the label, a graphic visualization of the partition in clusters is obtained. This kind of graphic representation is used often in two-dimensional spaces for representing partitions found with biclustering methods.

Keywords

Acute Myeloid Leukemia Acute Lymphoblastic Leukemia Generic Element Graphic Visualization Column Index 
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 Science+Business Media, LLC 2009

Authors and Affiliations

  • Antonio Mucherino
    • 1
  • Petraq J. Papajorgji
    • 1
  • Panos M. Pardalos
    • 2
  1. 1.Institute of Food & Agricultural Information Technology OfficeUniversity of FloridaGainesvilleUSA
  2. 2.Department of Industrial & Systems EngineeringUniversity of FloridaGainesvilleUSA

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