Cluster analysis is an essential tool in data mining. Several clustering algorithms have been
proposed and implemented, most of which are able to find good quality clustering results …

Data clustering is a popular unsupervised data mining tool that is used for partitioning a
given dataset into homogeneous groups based on some similarity/dissimilarity metric …

The financial sector is facing radical transformation. Leveraging digital technologies to offer
innovative services, FinTech start-ups are emerging in domains such as asset management …

Clustering is the partitioning of a set of objects into groups (clusters) so that objects within a
group are more similar to each others than objects in different groups. Most of the clustering …

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 …

The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

Cluster analysis aims at identifying groups of similar objects and, therefore helps to discover
distribution of patterns and interesting correlations in large data sets. It has been subject of …

One of fundamental challenges of clustering is how to evaluate results, without auxiliary
information. A common approach for evaluation of clustering results is to use validity …

We argue that there are many clustering algorithms, because the notion of" cluster" cannot
be precisely defined. Clustering is in the eye of the beholder, and as such, researchers have …

One of the most challenging aspects of clustering is validation, which is the objective and
quantitative assessment of clustering results. A number of different relative validity criteria …