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Unsupervised ranking of clustering algorithms by INFOMAX | PLOS ONE
Source: journals.plos.org
Article summary:
1. This paper proposes a metric for ranking the performance of clustering algorithms for a given dataset.
2. The proposed metric is based on Linsker's Infomax principle, which uses the entropy of the partition to rank clustering algorithms.
3. The results show that the ranking provided by the entropy of the partition is strongly correlated with the overlap with a ground truth partition.
Article analysis:
The article provides an interesting approach to ranking clustering algorithms based on Linsker’s Infomax principle and its application to hard clustering and community detection tasks. The authors provide evidence that their proposed metric is strongly correlated with a ground truth partition, as demonstrated by experiments on various datasets of different sizes and topologies.
The article appears to be well-researched and reliable, as it provides detailed descriptions of the methodology used in its experiments, as well as references to relevant literature in this field. Furthermore, all data used in this study are publicly available and can be accessed directly from links provided in the article.
However, there are some potential biases that should be noted when interpreting these results. For example, while the authors have tested their method on various datasets of different sizes and topologies, they have not explored how their method performs on datasets with more complex structures or larger numbers of clusters. Additionally, while they have compared their results against a ground truth partition, they do not provide any information about how this ground truth was obtained or what criteria were used to determine it. Finally, while they have provided evidence that their proposed metric is strongly correlated with a ground truth partition, they do not provide any evidence that it is superior to existing methods for ranking clustering algorithms.