Comparing High-Dimensional Partitions with the Co-clustering Adjusted Rand Index
Valerie Robert (),
Yann Vasseur () and
Vincent Brault ()
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Valerie Robert: Université Paris Saclay
Yann Vasseur: Université Paris Saclay
Vincent Brault: Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK
Journal of Classification, 2021, vol. 38, issue 1, No 11, 158-186
Abstract:
Abstract We consider the simultaneous clustering of rows and columns of a matrix and more particularly the ability to measure the agreement between two co-clustering partitions. The new criterion we developed is based on the Adjusted Rand Index and is called the Co-clustering Adjusted Rand Index named CARI. We also suggest new improvements to existing criteria such as the classification error which counts the proportion of misclassified cells and the Extended Normalized Mutual Information criterion which is a generalization of the criterion based on mutual information in the case of classic classifications. We study these criteria with regard to some desired properties deriving from the co-clustering context. Experiments on simulated and real observed data are proposed to compare the behavior of these criteria.
Keywords: Co-clustering; Adjusted rand index; Mutual information; Agreement; Partition (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)
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DOI: 10.1007/s00357-020-09379-w
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