# Selective clustering ensemble based on kappa and F-score

arXiv: https://arxiv.org/abs/2204.11062 · HTML: https://mlsanity.com/paper/2204.11062/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 55 references that are in the map.
- First posted on arXiv 23 Apr 2022.

## Builds on (most cited first, 5 of 5)

- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Evaluation of Community Detection Methods](https://mlsanity.com/paper/1807.01130/) (2018)
- [Weighted Clustering Ensemble: A Review](https://mlsanity.com/paper/1910.02433/) (2019)
- [Evaluating accuracy of community detection using the relative normalized mutual information](https://mlsanity.com/paper/1501.03844/) (2015)
- [WoCE: a framework for clustering ensemble by exploiting the wisdom of Crowds theory](https://mlsanity.com/paper/1612.06598/) (2016)

## Built on by (most cited first, 2 of 2)

- [Privacy-Preserving Federated Deep Clustering based on GAN](https://mlsanity.com/paper/2211.16965/) (2022)
- [Federated clustering with GAN-based data synthesis](https://mlsanity.com/paper/2210.16524/) (2022)

## Abstract (arXiv)

Clustering ensemble has an impressive performance in improving the accuracy and robustness of partition results and has received much attention in recent years. Selective clustering ensemble (SCE) can further improve the ensemble performance by selecting base partitions or clusters in according to diversity and stability. However, there is a conflict between diversity and stability, and how to make the trade-off between the two is challenging. The key here is how to evaluate the quality of the base partitions and clusters. In this paper, we propose a new evaluation method for partitions and clusters using kappa and F-score, leading to a new SCE method, which uses kappa to select informative base partitions and uses F-score to weight clusters based on stability. The effectiveness and efficiency of the proposed method is empirically validated over real datasets.