Purpose: This study proposes a new two-stage clustering method to break down the symmetric multiple traveling salesman …-programming model. A comparison with the k-means++ clustering algorithm, one of the most popular clustering algorithms, was made to … study tackles the issue of improving the performance of clustering-based optimization approaches in solving the mTSP by …

Expectation Maximization (EM) is a widely employed mixture model-based data clustering algorithm and produces … clustering algorithms. This paper presents an algorithm for the novel hybridization of EM and K-Means techniques for achieving … better clustering performance (NovHbEMKM). This algorithm first performs K-Means and then using these results it performs EM …

is based mostly on three cluster techniques like; K means, Fuzzy c-means and hierarchical clustering. The authors used … evolutionary techniques like genetic algorithms (GA) to extend the performance of the clustering model. The performance of these … obtained by the K-means clustering technique, 96.50%; whereas, Fuzzy C-means clustering received 93.50% and hierarchical …

-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes …

Data clustering is a key field of research in the pattern recognition arena. Although clustering is an unsupervised … learning technique, numerous efforts have been made in both hard and soft clustering. In hard clustering, K-means is the most … developed population based metaheuristic called Elitist based teaching learning based optimization (ETLBO) for data clustering …

The goal of the paper is to present the framework for combining clustering and classification for churn management in … approach to churn management using clustering and classification, which was tested on the churn dataset with a rich variable …

differences between countries in Europe according to how their companies tackled the challenges of IT security. Clustering is …