IEEE

WOA-DBSCAN: Application of Whale Optimization Algorithm in DBSCAN Parameter Adaption

Abstract: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a classic density-based clustering method that can identify clusters of arbitrary shapes in noisy datasets. However, ...
AlphaGalileo

Parallel AI slashes energy costs and carbon emissions in wind-solar-hydrogen power systems

A parallel deep reinforcement learning framework for wind-solar-hydrogen systems cuts operational costs by 6% and accelerates ...
EurekAlert!

A new era for green and intelligent transportation: LoRa meets distributed machine learning

In a world where urban traffic congestion and environmental concerns are escalating, innovative solutions are crucial for creating sustainable and efficient transportation systems. A groundbreaking ...
GitHub

Carson-Lam/DBSCAN-Optimization

In partnership with Andreas Züfle [1], this repository is an implementation for a proposed optimization of the largely popular DBSCAN [2]. This optimization aims to improve the time complexity of ...
LinkedIn

#202 Understanding DBSCAN: A Powerful Clustering Algorithm for Noisy Data

In this edition we will explore DBSCAN. Clustering is a fundamental task in data science and machine learning, used to group similar data points together. Traditional clustering methods, such as ...
Nature

Task offloading optimization in mobile edge computing based on a deep reinforcement learning algorithm using density clustering and ensemble learning

Mobile edge computing offloads compute-intensive tasks generated on mobile wireless devices (WD) to edge servers (ES), which provides mobile users with low-latency computing services. Opportunistic ...
Scientific Research Publishing

Research on Dense Crowd Area Detection Method Based on Improved YOLOv5 and Improved DBSCAN Clustering Algorithm ()

1 Tianjin University of Technology and Education, Tianjin, China. 2 Lvliang Vocational and Technical College, Lvliang, China. In modern society, dense crowd detection technology is particularly ...
Visual Studio Magazine

DBSCAN Clustering and Anomaly Detection Using C#

A good way to see where this article is headed is to take a look at the screenshot in Figure 1 and the graph in Figure 2. The demo program begins by loading a tiny 10-item dataset into memory. The ...
GitHub

Clustering-Algorithm-Test-Application

An application that lets you test different point clustering algorithms like K-Means, Affinity Propagation, DBSCAN and many more. In this repository I have included all of the .py files responsible ...
IEEE

SS-DBSCAN: Semi-Supervised Density-Based Spatial Clustering of Applications With Noise for Meaningful Clustering in Diverse Density Data

Abstract: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an unsupervised clustering algorithm designed to identify clusters of various shapes and sizes in noisy datasets by ...
Nature

Application of density clustering with noise combined with particle swarm optimization in UWB indoor positioning

Due to the presence of non-line-of-sight (NLOS) obstacles, the localization accuracy in ultra-wideband (UWB) wireless indoor localization systems is typically substantially lower. To minimize the ...
Visual Studio Magazine

DBSCAN Data Clustering from Scratch Using C#

Compared to other clustering techniques, DBSCAN does not require you to explicitly specify how many data clusters to use, explains Dr. James McCaffrey of Microsoft Research in this full-code, ...