Abstract
Quality Threshold is a clustering algorithm without specifying the number of clusters. It uses the maximum cluster diameter as the parameter to control the quality of clusters.
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Quality Threshold (QT) clustering (Heyer et al. 1999) is a partitioning clustering algorithm originally proposed for gene clustering. The focus of the algorithm is to find clusters with guaranteed quality. Instead of specifying K, the number of clusters, QT uses the maximum cluster diameter as the parameter.
The basic idea of QT is as follows: form a candidate cluster by starting with a random point and iteratively add other points, with each iteration adding the point that minimizes the increase in cluster diameter. The process continues until no point can be added without surpassing the diameter threshold. If surpassing the threshold, a second candidate cluster is formed by starting with a point and repeating the procedure. In order to achieve reasonable...
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Recommended Reading
Heyer L, Kruglyak S, Yooseph S (1999) Exploring expression data: identification and analysis of coexpressed genes. Genome Res 9:1106–1115
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© 2016 Springer Science+Business Media New York
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Jin, X., Han, J. (2016). Quality Threshold Clustering. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_692-1
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DOI: https://doi.org/10.1007/978-1-4899-7502-7_692-1
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Publisher Name: Springer, Boston, MA
Online ISBN: 978-1-4899-7502-7
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Latest
Quality Threshold Clustering- Published:
- 12 April 2023
DOI: https://doi.org/10.1007/978-1-4899-7502-7_692-2
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Original
Quality Threshold Clustering- Published:
- 10 June 2016
DOI: https://doi.org/10.1007/978-1-4899-7502-7_692-1