Designing efficient estimation of distribution algorithms for optimizing complex continuous problems is still a challenging task. Nowadays, histogram probabilistic model has become a hot topic in the ...

Aiming at the various distribute clustering problems in diffusion model for all data points, providing a new clustering algorithm (CDD) based on the change of density. CDD searches the core point ...

We introduce two clustering methods: k-means for graphs of the same size and gCEM, a model-based approach for graphs of different sizes. We evaluated their performance in toy models. Finally, we ...

Gaussian Mixture Model Clustering Report This report explores the implementation of a Gaussian Mixture Model (GMM) for clustering quantitative data, utilizing the Expectation-Maximization (EM) ...

A fast power interaction model within a cluster based on a consensus algorithm is established, and the micro-increase rate of dispatching cost is used as the consistency variable so that the cluster ...

To solve this problem, this paper proposed an improved multi-scale dense crowd detection method based on YOLOv5 and improved the DBSCAN clustering algorithm to identify densely crowded areas.