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 the process of identifying natural groupings or clusters within
multidimensional data based on some similarity measure. Clustering is a fundamental …

The k-means algorithm is generally the most known and used clustering method. There are
various extensions of k-means to be proposed in the literature. Although it is an …

Tire–pavement interaction noise (TPIN) accounts mainly for traffic noise, a sensitive
parameter affecting the eco-based maintenance decision outcome. Consistent methods or …

The validation of the results obtained by clustering algorithms is a fundamental part of the
clustering process. The most used approaches for cluster validation are based on internal …

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 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 …

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 …