Dec 1, 2016 · A new evaluation measure for co-clustering algorithms is presented. It satisfies the most exhaustive set of meta-evaluation conditions for co-clustering.
Dec 1, 2016 · In this work, we present MOCICE-BCubed F1, a new external measure for evaluating co-clusterings, in the scenario where gold standard ...
A new evaluation measure for co-clustering algorithms is presented.It satisfies the most exhaustive set of meta-evaluation conditions for co-clustering.
Cice-bcubed: A new evaluation measure for overlapping clustering algorithms ... 1, 2021. A new micro-objects-based evaluation measure for co-clustering algorithms.
Oct 7, 2024 · A new micro-objects-based evaluation measure for co-clustering algorithms. Pattern Recognit. Lett. 84: 142-148 (2016); 2015. [i1]. view.
In this paper, we propose a new external measure specifically designed for validating overlapping clusterings, which fulfills the main set of desirable ...
A new evaluation measure for co-clustering algorithms is presented.It satisfies the most exhaustive set of meta-evaluation conditions for co-clustering.
People also ask
Which of the following measures is suitable to evaluate the clustering quality?
What is evaluation of clustering algorithms in data mining?
Aug 16, 2024 · ... The χ-SIM [44] is a co-similarity-based learning approach that learns the row-wise and column-wise similarities in an The data matrix ...
A new micro-objects-based evaluation measure for co-clustering algorithms. Henry. Rosales-Méndez, and Yunior Ramírez-Cruz. Pattern Recognition Letters, 84 ...
A new micro-objects-based evaluation measure for co-clustering algorithms ... A new evaluation methodology based on the editing distance between output clusters ...