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Displaying all 26 assets.
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Description Experimental results
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description Brief information about the used datasets
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description Computing the stability of Cluster 1 of the partition in a considering the partition in b of the reference set using Max method
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description Accuracy of consensus partitions produced by cluster selection using the NMI-based cluster evaluator, the MAX-based cluster evaluator, the APMM-based cluster evaluator, and the ENMI-based cluster...
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description Four partitions π1–π4 are extracted from a simple dataset with 12 data points and two real clusters with k-means clustering. The k parameters in k-means is set to 3, 4, 2 and 2 respectively
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description The clusters extracted from partitions of Fig. 8
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description Computing the stability of Cluster 1 of the partition in a considering the partition in the b of the reference set using NMI method
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description The horizontal axis stands for the rate of stable clusters that are selected. The vertical axis is the same of Fig. 17 except employing original NMI measure as the stability measure of a cluster
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description Runtime in terms of data-set size
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description The performances of the proposed method compared with ten state of the art methods in terms of NMI validated by paired t-test (Dietterich 1998) with 95% level of confidence
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description Different methods for clustering ensemble
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description The horizontal axis stands for the rate of stable clusters that are selected using ENMI method. The vertical axis stands for averaged of the vertical axis of Figs. 14, 15 and 16
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description Different methods to perform the Step 4
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description The horizontal axis stands for the rate of stable clusters that are selected. The vertical axis is the same of Fig. 17 except employing APMM measure (Alizadeh et al. 2011) as the stability measure...
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description Halfring dataset
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description Computing the stability of Cluster 1 of the partition in a considering the partition in the b of the reference set using ENMI method
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description The horizontal axis stands for the rate of stable clusters that are selected. The vertical axis is the same of Fig. 17 except employing MAX measure (Alizadeh et al. 2011) as the stability measure...
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description The horizontal axis stands for the rate of stable clusters that are selected using ENMI method. The vertical axis stands for averaged NMI value over all ten datasets of Table 1
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description Computing the stability of Cluster Ci
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description The horizontal axis stands for the rate of stable clusters that are selected using ENMI method. The vertical axis stands for NMI value for Wins dataset
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description The horizontal axis stands for the rate of stable clusters that are selected using ENMI method. The vertical axis stands for averaged F-measure over all ten datasets of Table 1
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description The horizontal axis stands for the rate of stable clusters that are selected using ENMI method. The vertical axis stands for averaged accuracy value over all ten datasets of Table 1
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description The proposed clustering ensemble scheme
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description Different consensus functions
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description The time complexity of different algorithms
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review -
Description Computing the stability of Cluster 1 of the partition in a considering the partition in b of the reference set using APMM method
Article Title: Clustering ensemble selection considering quality and diversity
Publication Title: Artificial Intelligence Review
Displaying all 26 assets.