Data clustering involves identifying latent similarities within a dataset and organizing them
into clusters or groups. The outcomes of various clustering algorithms differ as they 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 …

This paper explores the problem of multi-view clustering, which aims to promote clustering
performance with multi-view data. The majority of existing methods have problems with …

In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult
task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic …

In this study a new internal clustering validation index is proposed. It is based on a measure
of the uniformity of the data in clusters. It uses the local density of each cluster, in particular …

The cluster evaluation process is of great importance in areas of machine learning and data
mining. Evaluating the clustering quality of clusters shows how much any proposed …

To evaluate clustering results is a significant part of cluster analysis. There are no true class
labels for clustering in typical unsupervised learning. Thus, a number of internal evaluations …

Graph clustering has attracted many interests in recent years, with numerous applications
ranging from the clustering of computer networks to the detection of social communities. It …

In modern industry, the production process is always accompanied by a large amount of
high-dimensional data, which is always too sparse to provide sufficient information for …

Most commonly, stability analyses are performed using an external validation measure. For
example, the Jaccard index is one of the indexes of choice for stability measurement. The …