… a comprehensive review of the different clustering algorithms and their applications. Due
to the large quantity of data maintained in several domains, clustering analysis has become …

… Many clustering methods use distance measures to determine the similarity or dissimilarity
between any pair of objects [22]. It is useful to denote the distance between two instances x i …

… for choosing clustering algorithms. In this paper, application of data clustering was systematically
discussed in view of the characteristics of the different clustering techniques that make …

… Therefore, the concept of automatic data clustering algorithms is … clustering algorithms refer
to any clustering techniques used to automatically determine the number of clusters without …

… clustering is to classify data into clusters in manner that data grouped are more similar. In this
paper, we provide an overview of various clustering techniques used for data analysis. … use …

… So far, we have discussed the application of clustering algorithms on static data. Nevertheless,
when analyzing data, it is important to take into account whether the data are dynamic or …

… In order to find the relevant number of clusters in each dataset, we use Nbclust package that
has been developed by [81]. It provides 30 indices available in SAS and R in one package. …

… After applying these algorithms to the datasets named letter, glass and wine, we … critical
review on why a specific clustering algorithm lacks with respect to execution time and clustering …

… In general applications, fuzzy clustering algorithms have been proved to be a … APPLICATIONS
REVIEW This section provides a table from a comprehensive review of the applications …

… clustering algorithms co-exists and are application specific. This paper majorly emphasises
on critical review of clustering algorithms … overview is also given about all major algorithms. …