Clustering is one of the main tasks of machine learning. Internal clustering validation
indexes (CVIs) are used to measure the quality of several clustered partitions to determine …

Clustering is a main task of data mining, and its purpose is to identify natural structures in a
dataset. The results of cluster analysis are not only related to the nature of the data itself but …

To evaluate clustering results is a significant part of cluster analysis. Since there are no true
class labels for clustering in typical unsupervised learning, many internal cluster validity …

Sažetak Internal cluster validity index is a powerful tool for evaluating clustering
performance. The study on internal cluster validity indices for categorical data has been a …

Clustering, an unsupervised pattern classification method, plays an important role in
identifying input dataset structures. It partitions input datasets into clusters or groups where …

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 …

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 validation has long been recognized as one of the vital issues essential to the
success of clustering applications. In general, clustering validation can be categorized into …

External cluster validity indices (CVIs) are used to quantify the quality of a clustering by
comparing the similarity between the clustering and a ground truth partition. However, some …

Different clustering algorithms with different parameter settings can produce various
partitions on the input data. Without the priori knowledge, it is difficult for users to select the …