Biclustering

Antonio Mucherino (), Petraq J. Papajorgji () and Panos M. Pardalos ()
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Antonio Mucherino: University of Florida
Petraq J. Papajorgji: University of Florida
Panos M. Pardalos: University of Florida

Chapter Chapter 7 in Data Mining in Agriculture, 2009, pp 143-160 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/978-0-387-88615-2_7

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