{"name": "Scipy Point Clustering", "package_name": "ScipyPointClustering", "description": "This plugin implements clustering for point data using the scipy module.", "about": "This plugin implements point custering in scipy and add a label integer\nfield to the feature class for the clustered data. Both hierarchical and\nk-means clustering are implemented.\nThis is a Procesing plugin (actuvated automatically) and can be found in\nthe processing toolbox.\nPlease note that there are memory limitations in hierarchical clustering -\nthe space required to create the clusters is O(n^2), which means that\nlarger datasets will run out of memory fast. As such there is a plugin\nsetting in the Processing options that sets the upper limit of points to\nprocess, by default set at 10,000. K-means is much more forgiving in terms\nof memory, so the limit is not enforced in those algorithms.\nAll credit to the scipy team for the original implementation of the cluster\nalgorithms.\nJones E, Oliphant E, Peterson P, et al. SciPy: Open Source Scientific\nTools for Python, 2001-, http://www.scipy.org/ [Online].\nseagull by Lane F. Kinkade from the Noun Project\nhttps://thenounproject.com/term/seagull/166081", "homepage": "https://github.com/SpatialVision/qgis_scipy_clustering", "repository": "https://github.com/SpatialVision/qgis_scipy_clustering", "tracker": "https://github.com/SpatialVision/qgis_scipy_clustering/issues", "author": "Henry Walshaw", "tags": ["vector"], "downloads": 11090, "latest_version": "0.2", "versions": [{"version": "0.2", "experimental": true, "qgis_min": "2.0.0", "qgis_max": "2.99.0", "downloads": 10010, "uploaded_by": "spatialvision", "upload_datetime": "2016-03-19T06:43:55.494301"}, {"version": "0.1", "experimental": true, "qgis_min": "2.0.0", "qgis_max": "2.99.0", "downloads": 1088, "uploaded_by": "spatialvision", "upload_datetime": "2016-03-18T11:32:37.689825"}]}