Clustering is an unsupervised process in data mining and pattern recognition and most of
the clustering algorithms are very sensitive to their input parameters. Therefore it is very …

Visual methods have been widely studied and used in data cluster analysis. Given a
pairwise dissimilarity matrix\schmi D of a set of n objects, visual methods such as the VAT …

Cluster validation in Clustering is an open problem. The most exploited possibility is the
validation through cluster validity indexes (CVIs). However, there are many indexes …

Clustering is a research challenge focused on discovering knowledge from data samples
whose goal is to build good quality partitions. In this paper is proposed an approach based …

Similarity measures for comparing clusterings is an important component, eg, of evaluating
clustering algorithms, for consensus clustering, and for clustering stability assessment …

In unsupervised machine learning, agreement between partitions is commonly assessed
with so-called external validity indices. Researchers tend to use and report indices that …

Clustering, one of the most important unsupervised learning problems, is the task of dividing
a set of objects into clusters such that objects within the same cluster are similar while …

A key issue in cluster analysis is the choice of an appropriate clustering method and the
determination of the best number of clusters. Different clusterings are optimal on the same …

Over the last decades, a great variety of data mining techniques have been developed to
reach goals concerning Knowledge Discovery in Databases. Among them, cluster detection …

We review two clustering algorithms (hard c-means and single linkage) and three indexes of
crisp cluster validity (Hubert's statistics, the Davies-Bouldin index, and Dunn's index). We …