Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …

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 …

This paper introduces an innovative automatic K-means clustering algorithm, namely HGA-
FACO, which seamlessly integrates the noise algorithm, Genetic Algorithm (GA), Ant Colony …

The partitional clustering concept started with K-means algorithm which was published in
1957. Since then many classical partitional clustering algorithms have been reported based …

In cluster analysis, a fundamental problem is to determine the best estimate of the number of
clusters; this is known as the automatic clustering problem. Because of lack of prior domain …

Abstract Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm,
called Krill Herd (KH), for global optimization [Gandomi AH, Alavi AH. Krill Herd: A New Bio …

Today's data mostly does not include the knowledge of cluster number. Therefore, it is not
possible to use conventional clustering approaches to partition today's data, ie, it is …

Data objects with mixed numeric and categorical attributes are commonly encountered in
real world. The k-prototypes algorithm is one of the principal algorithms for clustering this …

This study presents an improved krill herd (IKH) approach to solve global optimization
problems. The main improvement pertains to the exchange of information between top krill …

Kmeans clustering algorithm is an iterative unsupervised learning algorithm that tries to
partition the given dataset into k pre-defined distinct non-overlap** clusters where each …