Hierarchical agglomerative |
Forms clusters starting at lowest level12
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Hierarchical divisive |
Forms clusters starting at the highest level14,15
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Self organising maps |
Partitions genes/experiments into prespecific number of groups by mapping onto nodes16,17
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K-means |
Partitions genes/experiments into prespecific number of groups by finding centroids7
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Quality cluster algorithm |
Forms groups based on diameter quality measure and jackknife correlations18
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Super vector machines |
Machine learning process that incorporates non-array information19
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Singular value decomposition |
Groups genes/experiments by reducing data matrix to eigen vectors (similar to principal component analysis)20,21
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Gene shaving |
Seeks groups of genes to maximise variation among experiments22
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Class prediction |
Identifies genes whose behaviour predicts defined classes23
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