A popular method for selecting the number of clusters is based on stability arguments: one
chooses the number of clusters such that the corresponding clustering results are" most …

Stability is a common tool to verify the validity of sample based algorithms. In clustering it is
widely used to tune the parameters of the algorithm, such as the number k of clusters. In …

Many clustering algorithms, including cluster ensembles, rely on a random component.
Stability of the results across different runs is considered to be an asset of the algorithm. The …

We consider the stability of k-means clustering problems. Clustering stability is a common
heuristics used to determine the number of clusters in a wide variety of clustering …

The concept of cluster stability is introduced as a means for assessing the validity of data
partitionings found by clustering algorithms. It allows us to explicitly quantify the quality of a …

Cluster analysis remains one of the most challenging yet fundamental tasks in unsupervised
learning. This is due in part to the fact that there are no labels or gold standards by which …

We introduce a novel method for assessing the robustness of clusters found by partitioning
algorithms. First, we show how the stability of individual objects can be estimated based on …

Stability in cluster analysis is strongly dependent on the data set, especially on how well
separated and how homogeneous the clusters are. In the same clustering, some clusters …

Here the problem of selecting the number of clusters in cluster analysis is considered.
Recently, the concept of clustering stability, which measures the robustness of any given …

The goal of this paper is to discuss statistical aspects of clustering in a framework where the
data to be clustered has been sampled from some unknown probability distribution. Firstly …