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1.
Figure 1

Figure 1. From: Evaluation of gene-expression clustering via mutual information distance measure.

A normal-shape frequencies of MI-Separation scores. A normal-shape frequencies of the MI-based separation scores of the single-error group of solutions for dataset 1 that contains 1000 genes from 51 sampled tissues.

Ido Priness, et al. BMC Bioinformatics. 2007;8:111-111.
2.
Figure 6

Figure 6. From: Evaluation of gene-expression clustering via mutual information distance measure.

MI-based scores of clustering solutions with 5 clusters. Efficiency frontiers for solutions with 5 clusters, obtained by the K-means, the SOM, the sIB and the Click clustering algorithms. The clusters are obtained over the Yeast cell-cycle dataset with 800 genes and 72 experimental time-conditions. The scores are depicted on the MI-based Homogeneity-Separation plane.

Ido Priness, et al. BMC Bioinformatics. 2007;8:111-111.
3.
Figure 7

Figure 7. From: Evaluation of gene-expression clustering via mutual information distance measure.

MI-based scores of clustering solutions with 7 clusters. Efficiency frontiers for solutions with 7 clusters, obtained by the K-means, the SOM, the sIB and the Click clustering algorithms. The clusters are obtained over the Yeast cell-cycle dataset with 800 genes and 72 experimental time-conditions. The scores are depicted on the MI-based Homogeneity-Separation plane.

Ido Priness, et al. BMC Bioinformatics. 2007;8:111-111.
4.
Figure 2

Figure 2. From: Evaluation of gene-expression clustering via mutual information distance measure.

Homogeneity Z-scores for dataset 1. Normalized homogeneity Z-scores of clustering solutions with different number of errors based on the Pearson correlation, the Euclidean distance and the MI measures for dataset 1 that contains 1000 genes from 51 sampled tissues.

Ido Priness, et al. BMC Bioinformatics. 2007;8:111-111.
5.
Figure 4

Figure 4. From: Evaluation of gene-expression clustering via mutual information distance measure.

Homogeneity Z-scores for dataset 2. Normalized homogeneity Z-scores of clustering solutions with different number of errors based on the Pearson correlation, the Euclidean distance and the MI measures for dataset 2 that contains 1000 genes from 54 sampled tissues.

Ido Priness, et al. BMC Bioinformatics. 2007;8:111-111.
6.
Figure 8

Figure 8. From: Evaluation of gene-expression clustering via mutual information distance measure.

Pearson correlation based scores of solutions with 5 clusters. Efficiency frontiers for solutions with 5 clusters, obtained by the K-means, the SOM, the sIB and the Click clustering algorithms. The clusters are obtained over the Yeast cell-cycle dataset with 800 genes and 72 experimental time-conditions. The scores are depicted on the Pearson correlation based Homogeneity-Separation plane.

Ido Priness, et al. BMC Bioinformatics. 2007;8:111-111.
7.
Figure 3

Figure 3. From: Evaluation of gene-expression clustering via mutual information distance measure.

Separation Z-scores for dataset 1. Normalized separation Z-scores of clustering solutions with different number of errors based on the Pearson correlation, the Euclidean distance and the MI measures for dataset 1 that contains 1000 genes from 51 sampled tissues.

Ido Priness, et al. BMC Bioinformatics. 2007;8:111-111.
8.
Figure 5

Figure 5. From: Evaluation of gene-expression clustering via mutual information distance measure.

Separation Z-scores for dataset 2. Normalized separation Z-scores of clustering solutions with different number of errors based on the Pearson correlation, the Euclidean distance and the MI measures for dataset 2 that contains 1000 genes from 54 sampled tissues.

Ido Priness, et al. BMC Bioinformatics. 2007;8:111-111.
9.
Figure 9

Figure 9. From: Evaluation of gene-expression clustering via mutual information distance measure.

Pearson correlation based scores of solutions with 7 clusters. Efficiency frontiers for solutions with 7 clusters, obtained by the K-means, the SOM, the sIB and the Click clustering algorithms. The clusters are obtained over the Yeast cell-cycle dataset with 800 genes and 72 experimental time-conditions. The scores are depicted on the Pearson correlation based Homogeneity-Separation plane.

Ido Priness, et al. BMC Bioinformatics. 2007;8:111-111.

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