Image segmentation has been considered as the first step in the image processing. An
efficient segmentation result would make it easier for further analysis of image processing …

Data clustering is related to the split of a set of objects into smaller groups with common
features. Several optimization techniques have been proposed to increase the performance …

Accurate and effective localization of reliability-critical gates (RCGs) is one of the important
prerequisites for low-cost circuit fault tolerance in the early stages of circuit design. This …

With the ever-growing data traffic in computer networks nowadays, the management of large-
scale networks is a challenge for guaranteeing the quality of the provided services. This is …

This paper proposed a new methodology to perform cluster analysis based on genetic
algorithm (GA). Firstly, the population of GA is initialized by k-means algorithm to reach the …

The main objective of e-learning systems is to improve the student learning performance
and satisfaction. This can be achieved by providing a personalized learning experience that …

This paper proposes a hybrid approach for solving data clustering problems. This hybrid
approach used one of the swarm intelligence algorithms (SIAs): grasshopper optimization …

This paper proposed a novel genetic algorithm (GA) based k-means algorithm to perform
cluster analysis. In the proposed approach, the population of GA is initialized by k-means …

With the increasing popularity of location-based social networks (LBSNs), community
detection has emerged as an important and practical issue. One of the main shortcomings of …

Data clustering is a recognized data analysis method in data mining whereas K-Means is
the well known partitional clustering method, possessing pleasant features. We observed …