Conventional cluster validity techniques usually represent all the validity information available about a particular clustering by a single numbel. The display method introduced here uses images generated lrom the results of any prototype generator clustering algorithm to do cluster validation.

Many external and internal validity measures have been proposed in order to estimate the number of clusters in gene expression data but as a rule they do not consider the analysis of the stability of the groupings produced by a clustering algorithm. Based on the approach assessing the predictive power or stability of a partitioning, we propose the new measure of cluster validation and the selec...

Although the goal of clustering is intuitively compelling and its notion arises in many fields, it has been difficult to define a unified approach to address the clustering problem and thus diverse clustering approaches abound in the research community. These approaches are based on different clustering principles and assumptions and they often lead to qualitatively different results. As a cons...

Clustering aims at discovering groups and identifying interesting distributions and patterns in data sets. Researchers have extensively studied clustering since it arises in many application domains in engineering and social sciences. In the last years the availability of huge transactional and experimental data sets and the arising requirements for data mining created needs for clustering algo...

Many validity measures and learning algorithms have been applied for quantitatively evaluating the performance of clustering algorithms. However, existing validity measures and clustering evaluation are based on a single realization, and lack of generality. These tasks involve more than one criterion, so it can be modelled as a multiple criteria decision making (MCDM) problem. In this paper, we...

Abstract We analyse football (soccer) player performance data with mixed type variables from the 2014-15 season of eight European major leagues. cluster these based on a tailor-made dissimilarity measure. In order to decide between many available clustering methods and choose an appropriate number clusters, we use approach by Akhanli Hennig (2020. “Comparing Clusterings Numbers Clusters Aggrega...

A considerable amount of work has been dedicated to clustering numerical data sets, but only a handful of categorical clustering algorithms are reported to date. Furthermore, almost none has addressed the following two important cluster validity problems: (1) Given a data set and a clustering algorithm that partitions the data set into k clusters, how can we determine the best k with respect to...