Because clustering is an unsupervised learning task, a number of different validity indices
have been proposed to measure the quality of the clustering results. However, there is no …

Cluster validity evaluation is a hot issue in clustering algorithm research. Aiming at
determining the optimal number of clusters in cluster validity evaluation, this paper proposes …

The evaluation on clustering results is an important component of clustering analysis, which
can be conducted by the cluster validity index. However, the performances of most existing …

It is crucial to evaluate the quality of clustering results in cluster analysis. Although many
cluster validity indices (CVIs) have been proposed in the literature, they have some …

Many validity indices have been proposed for quantitatively assessing the performance of
clustering algorithms. One limitation of existing indices is their lack of generalizability, due to …

The evaluation of clustering results plays an important role in clustering analysis and usually
is completed by a validity index or several. But currently existing validity indexes are …

Clustering validation and identifying the optimal number of clusters are of great importance
in expert and intelligent systems. However, the commonly used similarity measures for …

In unsupervised learning tasks, one of the most significant and challenging aspects is how to
estimate the optimal number of clusters (NC) for a particular set of data. Identifying NC in a …

Clustering validation is concerned with assessing the quality of clustering solutions. Since
clustering is unsupervised and highly explorative, clustering validation has been an …

Internal validity indices are crucial in evaluating the quality of clustering results, serving as
valuable tools for comparing various clustering algorithms and determining the optimal …