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Biclustering

In: Data Mining in Agriculture

Author

Listed:
  • Antonio Mucherino

    (University of Florida)

  • Petraq J. Papajorgji

    (University of Florida)

  • Panos M. Pardalos

    (University of Florida)

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.

Suggested Citation

  • Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "Biclustering," Springer Optimization and Its Applications, in: Data Mining in Agriculture, chapter 0, pages 143-160, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-88615-2_7
    DOI: 10.1007/978-0-387-88615-2_7
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