  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.
