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Table 3 Biclustering algorithms considered for this study. Additionally, they will be compared to three popular clustering algorithms: k-means, spectral, and ward’s hierarchical methods. For clustering, we use scikit-learn implementations [64]

From: Biclustering fMRI time series: a comparative study

Algorithm Type of search Available at References Reason to choose it
BicPAM Exhaustive BicPAMS [39, 62] State-of-the-art pattern mining based biclustering method
CCC Exhaustive BiGGEsTS [40, 61] Allows to obtain temporal contiguous biclusters efficiently
ISA Greedy isa2 [35] State-of-the-art greedy algorithm able to deal with real data
XMotifs Greedy biclust [36, 63] State-of-the-art greedy algorithm based on a strategy of discretizating data
Bimax Divide and conquer biclust [30, 63] Very fast algorithm able to detect simple structures
FABIA Distribution parameter identification FABIA [38] State-of-the-art algorithm
Spectral Biclustering Distribution parameter identification biclust [37, 63] State-of-the-art algorithm able to detect a specific type of bicluster structures