K-means clustering algorithm is a partitional clustering algorithm that has been used widely
in many applications for traditional clustering due to its simplicity and low computational …

The increase in available data has attracted the interest in clustering approaches as a way
of coherently aggregating them and identify patterns in big data. Hence, Swarm Intelligence …

The fuzzy c-means (FCM) algorithm is a popular method for data clustering and image
segmentation. However, the main problem of this algorithm is that it is very sensitive to the …

The primary objective of this paper is to develop a methodology for brain tumor
segmentation. Nowadays, brain tumor recognition and fragmentation is one among the …

Data present in abundance increases the complexity of handling them, which affects the
effective decision-making process. Hence, data clustering gains remarkable importance in …

Automatic clustering based hybrid metaheuristic algorithms has attracted the center of
interest of scientists and engineers which become a hot topic for different data analysis …

In this paper, a new clustering algorithm inspired by magnetic force is proposed. This
algorithm is not sensitive to the initialization problem of cluster centroids. Centroid particles …

An efficient and energy-saving algorithm, K-means and FAH (KAF), has been proposed to
solve the problems of node energy constraints, short network cycle and low throughput in …

Nowadays, the efficiency of Machine Learning (ML) mechanisms in the Internet of Things
(IoT) prompts the researchers and developers to use these emerging technology in different …

Requirement engineering is the base phase of any software project, since this phase is
concerned about requirements identification, processing and manipulation. The main source …