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ClusterPoints QGIS 4 compatibility
Not QGIS 4 ready Active
Not yet — the latest version declares max QGIS version 3.99.0, so it is not offered to QGIS 4 users.
Cluster Points conducts clustering of points based on their mutual distance to each other or based on supplemental information from attributes. The user can select between the K-Means or Fuzzy C-Means algorithms and (agglomerative) hierarchical clustering with several different link functions. To find the best possible grouping of points, both geographical coordinates and numerical fields can be incorporated into the algorithm to a varying degree.
About
Sometimes it is requisite to determine distinct groups of points on a map which are closest to each other. To automatize this process, Cluster Points allows you to find a predefined number of groups with mutually close points. Different cluster algorithms are provided in this comprehensive GIS implementation. The user may choose from the K-Means or Fuzzy C-Means algorithms, hierarchical clustering with SLINK or hierarchical clustering with the Lance-Williams distance updates together with a cluster feature preprocessing (similar to BIRCH). All versions output an explicit cluster ID for individual points. The Fuzzy C-Means algorithm additionally outputs cluster membership probabilities for individual points. The user is offered the option to prescribe multiple numericals fields which can be incorporated into the spatial clustering by a prescribed percentage. Depending on this percentage, the clustering is rather location-based or attribute-based. <br><br>This plugin was started during the project phase of a GIS-Analyst training course in Berlin (https://gis-trainer.de>https://gis-trainer.de).
| Author | Johannes Jenkner |
|---|---|
| Downloads | 38,737 |
| Latest version | 6.2 |
| Last updated | 2026-08-10 (67 days ago) |
| Min QGIS | 3.32.0 |
| Max QGIS | 3.99.0 |
| QGIS 4 ready | No |
| Maintenance | Active |
| Categories | Spatial analysis, Vector tools, AI & machine learning |
| Tags | hierarchical, k-means, points, clustering, machine learning, cluster, grouping, birch, link function, c-means |
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Maintenance: Active = updated within 1 year, Quiet = 1–3 years, Likely abandoned = over 3 years.