The detection and parametrization of molecular clumps is the first step in studying them. We propose a method based on Local Density Clustering algorithm while physical parameters those are measured using Multiple Gaussian Model algorithm. One advantage applying to clump segmentation, high accuracy under different signal-to-noise levels. able deal with overlapping whose can be derived reliably....

Density-based clustering algorithms have been the most commonly used for discovering regions and points of interest in cities using global positioning system (GPS) information geo-tagged photos. However, users sometimes find more specific areas real objects captured pictures. Recent advances deep learning technology make it possible to recognize these since detection is a very time-consuming ta...

Many clustering algorithms fail when clusters are of arbitrary shapes, varying densities, or the data classes unbalanced and close to each other, even in two dimensions. A novel algorithm, DenMune is presented meet this challenge. It based on identifying dense regions using mutual nearest neighborhoods size K, where K only parameter required from user, besides obeying neighbor consistency princ...