DBSCAN is a density-based clustering algorithm first described in Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu (1996). "A density-based algorithm for discovering clusters in large spatial databases with noise". In Evangelos Simoudis, Jiawei Han, Usama M. Fayyad. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI Press. pp. 226–231 defines three types of points in a dataset. Core Points are points that have at least a minimum number of neighbors (MinPts) within a specified distance (eps). Border Points are points that are within eps of a core point, but have less than MinPts neighbors. Noise Points are neither core points nor border points.
Clusters are built by joining core points to one another. If a core point is within eps of another core point, they are termed directly density-reachable.) All points that are within eps of a core point are termed density-reachable and are considered to be part of a cluster. All others are considered to be noise.
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
A zipped version of the software site can be downloaded here.
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to email@example.com, follow @NodePit on Twitter, or chat on Gitter!
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.