QGIS Python Plugins Repository
Plugins tagged with: clustering
7 records found —
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| Name | Featured | Downloads | Author ↓ | Latest Plugin Version | Created on | Stars (votes) | Stable | Exp. | |
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pqkmeans-clustering | — | 155 | armstrong ngolo | 2024-02-14T03:07:23.545558+00:00 | 2024-02-14T03:07:21.790258+00:00 | (1) |
— | 0.1 |
| This clustering algorithm is a quantized version of the K-Means algorithm that is memory and computationaly more efficient. | |||||||||
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Clusterpy - Spatially constrained clustering | — | 14175 | RISE Group Universidad EAFIT | 2014-06-08T03:10:47.111145+00:00 | 2014-06-08T03:10:46.681937+00:00 | (38) |
1.0 | — |
| Clusterpy plugin version for QGIS | |||||||||
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ClusterPoints | — | 26698 | Johannes Jenkner | 2023-08-18T05:52:37.378747+00:00 | 2020-03-30T11:15:13.154597+00:00 | (27) |
6.1 | — |
| 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. | |||||||||
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Webmap Utilities | — | 2798 | Guilherme Alexsander Pereira | 2023-09-29T12:44:52.331069+00:00 | 2023-09-29T12:44:51.660169+00:00 | (4) |
0.1 | — |
| This plugin provides tools for clustered and hierarchical visualization of vector layers, creation of Relief Shading and management of scales using zoom levels. | |||||||||
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Attribute based clustering | — | 59990 | Eduard Kazakov | 2022-11-28T15:21:32.882328+00:00 | 2016-04-27T01:46:32.306565+00:00 | (40) |
2.2.1 | — |
| Plugin produces clustering for features of vector layer based on unlimited number of attributes using hierarchical or k-means algorithms. Hierarchical algorithm is available in two variations: native realization for known number of clusters and scipy realization for unknown number of clusters. K-means method requieres scipy and known number of clusters. You can define weight for every attribute, if native hierarchical clustering used. Elbow method for estimating optimal number of clusters is available. Also calculating of distances from each object to centroids of each cluster is available. | |||||||||
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EnMAP-Box 3 | — | 64500 | Andreas Janz, Benjamin Jakimow, Sebastian van der Linden, Fabian Thiel, Henrike Dierkes | 2024-04-11T13:30:16.661875+00:00 | 2019-03-07T11:24:07.383848+00:00 | (98) |
3.13.3 | 3.14.1 |
| Imaging Spectroscopy and Remote Sensing for QGIS | |||||||||
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Cluster Analysis | — | 9906 | Andrea Folini | 2022-06-21T17:16:13.903622+00:00 | 2022-02-11T13:39:55.373892+00:00 | (11) |
1.0.1 | — |
| Perform attribute based clustering on vector layers | |||||||||
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