A package for combining multiple partitions into a consolidated clustering. The combinatorial optimization problem of obtaining such a consensus clustering is reformulated in terms of approximation algorithms for graph or hyper-graph partitioning.
Hello,
I ran cluster_ensembles algorithm on 3 outputs from 3 different clustering algorithms. The adjusted Rand index of these 3 outputs was 0.75, 0.75 and 0.78 when compared to true labels. The adjusted Rand index of the output from the cluster_ensembles was 0.18. The dataset has about 650 data points and 9 clusters.
I was wondering if you have any thoughts on why your algorithm didn't perform well. I expected adjusted Rand index of the result to be at least not worse than the individual inputs.
Hello, I ran cluster_ensembles algorithm on 3 outputs from 3 different clustering algorithms. The adjusted Rand index of these 3 outputs was 0.75, 0.75 and 0.78 when compared to true labels. The adjusted Rand index of the output from the cluster_ensembles was 0.18. The dataset has about 650 data points and 9 clusters.
I was wondering if you have any thoughts on why your algorithm didn't perform well. I expected adjusted Rand index of the result to be at least not worse than the individual inputs.
Let me know if you need additional information.
Helena