This paper studies the generalization and normalization issues of information-theoretic
distance measures for clustering validation. Along this line, we first introduce a uniform …

Everyday a large number of records of surfing internet are generated. In various situations
when the authors are analysing internet data they do not know the cluster structure of the …

As an unsupervised pattern classification method, clustering partitions the input datasets into
groups or clusters. It plays an important role in identifying the natural structure of the target …

Clustering data is a major task in machine learning. From a user's perspective, one
particular challenge in this area is the differentiation of at least two clusterings. This is …

Internal cluster validity measures (such as the Calinski–Harabasz, Dunn, or Davies–Bouldin
indices) are frequently used for selecting the appropriate number of partitions a dataset …

It is critical to evaluate the quality of clusters for most cluster analysis. A number of cluster
validity indexes have been proposed, such as the Silhouette and Davies-Bouldin indexes …

The true cluster number of the dataset in practical applications is rarely known in advance.
Therefore, it is necessary to use a cluster validity index to evaluate the clustering results and …

Clustering validation and identifying the optimal number of clusters are crucial in expert and
intelligent systems. However, the commonly used cluster validity indices (CVI) are not …

Automated machine learning (AutoML) aims to minimize human intervention during a
machine learning task, for example by means of automatic algorithm selection and its …

Measuring the quality of data partitions is essential to the success of clustering applications.
A lot of different validity indices have been proposed in the literature, but choosing the …