… shift in the choice of clustering methods among domain experts … clustering algorithms still
depend on the number of clusters provided a priori. These conventional clustering algorithms …

… a machine to solve problems. We are in the age of machine learning where machines solve
… us better than we know ourselves, here also a machine learning algorithm is in action [18]. …

… with conceptual clustering algorithms. Some well-known clustering based algorithms found
in the literature include UNIMEM, COBWEB, CLASSIT, CLASSWEB, CLUSTER/2, and WITT. …

… As stated in [24], machine learning provides the technical basis of data mining by … Machine
learning is divided into two primary sub-fields: supervised learning and unsupervised learning…

… It is also an unsupervised learning approach to machine learning. From statistical viewpoint,
… a learning procedure for the k-means clustering algorithm. This learning procedure can …

… In this paper, we investigate and develop a supervised clustering algorithm that uses the
class information rather than arbitrary parameters to guide the clustering procedure. …

… of the proposed clustering algorithms … clustering methods applied in various fields. In, the
author researched the datasets occurring in statistics, computer science and machine learning. …

… clustering algorithms widely used today in different frameworks for data science and related
areas. The objective of this paper is to propose and derive a novel clustering algorithm by …

… We then present alternative clustering algorithms including Gaussian mixture model and …
of the other cluster algorithms rely on the same requirement. There are some algorithms that do …

… Cluster analysis, primitive exploration with little or no prior knowledge, consists of … clustering
algorithms for data sets appearing in statistics, computer science, and machine learning, …