Data clustering is a popular unsupervised data mining tool that is used for partitioning a
given dataset into homogeneous groups based on some similarity/dissimilarity metric …

A new trend of problem formulation for image segmentation is to use multiobjective
optimization approach in its decision making process. Multiobjective formulations are …

Multiobjective optimization methods in image analysis are one of the active research
domains in the current years. These methods are used for the decision-making process in …

Accurate segmentation of brain tissues in magnetic resonance imaging (MRI) data plays
critical role in the clinical diagnostic and treatment planning. The presence of noise and …

This study reviews the state-of-the-art multiobjective optimisation (MOO) techniques with
metaheuristic through clustering approaches developed specifically for image segmentation …

Clustering is a difficult task: there is no single cluster definition and the data can have more
than one underlying structure. Pareto-based multi-objective genetic algorithms (eg, MOCK …

In this paper, we propose a grayscale image segmentation method based on a
multiobjective optimization approach that optimizes two complementary criteria (region and …

The framework of multi-objective clustering can serve as a competent technique in
nowadays human issues ranging from decision making process to machine learning and …

Image segmentation aims to partition an image into several disjointed regions that are
homogeneous with regards to some measures so that subsequent higher level computer …

No clustering algorithm is guaranteed to find actual groups in any dataset. Thus, the
selection of the most suitable clustering algorithm to be applied to a given dataset is not …