Issues relating to clustering today are computational techniques, optimization, and
performance of clustering algorithms. In this research, a metaheuristic grouping method of …

Data clustering is an unsupervised learning method essential in many different sciences.
The main goal of clustering is to find the similarity between samples of a data set to form …

Data clustering has emerged as a major problem in areas such as deep learning
applications, computer vision process, big data which have become trending especially in …

Clustering is a powerful technique in data-mining, which involves identifing homogeneous
groups of objects based on the values of attributes. Meta-heuristic algorithms such as …

Clustering, which is handled by many researchers, is separating data into clusters without
supervision. In clustering, the data are grouped using similarities or differences between …

K-Means is a widely used algorithm among many clustering algorithms. However, in the
process of clustering by K-Means, there are problems, such as the difficulty of selecting K …

Clustering is a technique of grouping the data objects into clusters. Many metaheuristic
algorithms based on swarm intelligence, physic laws, and chemical reactions, among …

Abstract This chapter presents Hybrid Whale Optimization Algorithm (HWOA) to tackle the
stubborn problems of local optima traps and initialization sensitivity of the K-means …

Clustering ensemble is an open proposition that aims to solve the limitation of a single
cluster algorithm on the diversity of data structures in data partitions. It obtains a consensus …

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