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 ...
IEEE

Identifiability Improvement for Minor Defect in Cables Using FMCW and Adaptive Density-Based Clustering

Abstract: During the process of defect identification in cables, the conventional frequency domain reflectometry (FDR) method directly determines the defect position through the localization spectrum, ...
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 ...
GitHub

AI Code Generation Prompts Examples (Python)

The purpose of this repository is to provide a few sample prompts used in order to create a simple Python GUI for the Linux desktop project. I created this repository and wrote these prompts on March ...