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Image analysis software and hardware

COMPARE provides access to state-of-the-art high performance computing hardware for applying the latest methods from image analysis and machine learning research. We have expertise in a wide range of ...
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 ...
GitHub

R package dbscan - Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms

This R package (Hahsler, Piekenbrock, and Doran 2019) provides a fast C++ (re)implementation of several density-based algorithms with a focus on the DBSCAN family for clustering spatial data. The ...
IEEE

DBSCAN Algorithm Clustering for Massive AIS Data Based on the Hadoop Platform

Abstract: With the rapid development of economy in central cities, Inland river traffic flow has grown rapidly, based on the vast amount of ship AIS information, there are a lot of inland river ...
GitHub

A developed version of DBSCAN (Semi-Supervised Clustering)

To run the code you need to pass 4 arguments ( dataset as csv with last column as the class. eps value, MinPts value and a text file name to save the classes). #run call example :python3 SSDBSCAN.py ...
C&EN

AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High-Throughput Screens

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