[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[[["\u003cp\u003eGroups features within a collection into clusters based on a provided clusterer.\u003c/p\u003e\n"],["\u003cp\u003eAssigns each feature a cluster ID, stored in a new property with a user-defined name (defaults to "cluster").\u003c/p\u003e\n"],["\u003cp\u003eRequires a trained clusterer and a FeatureCollection where each feature contains the necessary properties for clustering.\u003c/p\u003e\n"],["\u003cp\u003eReturns a new FeatureCollection with the added cluster ID property.\u003c/p\u003e\n"]]],[],null,["# ee.FeatureCollection.cluster\n\nClusters each feature in a collection, adding a new column to each feature containing the cluster number to which it has been assigned.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|--------------------------------------------------------|-------------------|\n| FeatureCollection.cluster`(clusterer, `*outputName*`)` | FeatureCollection |\n\n| Argument | Type | Details |\n|------------------|----------------------------|----------------------------------------------------------------------------------------------------------------|\n| this: `features` | FeatureCollection | The collection of features to cluster. Each feature must contain all the properties in the clusterer's schema. |\n| `clusterer` | Clusterer | The clusterer to use. |\n| `outputName` | String, default: \"cluster\" | The name of the output property to be added. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n// Import a Sentinel-2 surface reflectance image.\nvar image = ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG');\n\n// Get the image geometry to define the geographical bounds of a point sample.\nvar imageBounds = image.geometry();\n\n// Sample the image at a set of random points; a feature collection is returned.\nvar pointSampleFc = image.sample(\n {region: imageBounds, scale: 20, numPixels: 1000, geometries: true});\n\n// Instantiate a k-means clusterer and train it.\nvar clusterer = ee.Clusterer.wekaKMeans(5).train(pointSampleFc);\n\n// Cluster the input using the trained clusterer; optionally specify the name\n// of the output cluster ID property.\nvar clusteredFc = pointSampleFc.cluster(clusterer, 'spectral_cluster');\n\nprint('Note added \"spectral_cluster\" property for an example feature',\n clusteredFc.first().toDictionary());\n\n// Visualize the clusters by applying a unique color to each cluster ID.\nvar palette = ee.List(['8dd3c7', 'ffffb3', 'bebada', 'fb8072', '80b1d3']);\nvar clusterVis = clusteredFc.map(function(feature) {\n return feature.set('style', {\n color: palette.get(feature.get('spectral_cluster')),\n });\n}).style({styleProperty: 'style'});\n\n// Display the points colored by cluster ID with the S2 image.\nMap.setCenter(-122.35, 37.47, 9);\nMap.addLayer(image, {bands: ['B4', 'B3', 'B2'], min: 0, max: 1500}, 'S2 image');\nMap.addLayer(clusterVis, null, 'Clusters');\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# Import a Sentinel-2 surface reflectance image.\nimage = ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG')\n\n# Get the image geometry to define the geographical bounds of a point sample.\nimage_bounds = image.geometry()\n\n# Sample the image at a set of random points a feature collection is returned.\npoint_sample_fc = image.sample(\n region=image_bounds, scale=20, numPixels=1000, geometries=True\n)\n\n# Instantiate a k-means clusterer and train it.\nclusterer = ee.Clusterer.wekaKMeans(5).train(point_sample_fc)\n\n# Cluster the input using the trained clusterer optionally specify the name\n# of the output cluster ID property.\nclustered_fc = point_sample_fc.cluster(clusterer, 'spectral_cluster')\n\ndisplay(\n 'Note added \"spectral_cluster\" property for an example feature',\n clustered_fc.first().toDictionary(),\n)\n\n# Visualize the clusters by applying a unique color to each cluster ID.\npalette = ee.List(['8dd3c7', 'ffffb3', 'bebada', 'fb8072', '80b1d3'])\ncluster_vis = clustered_fc.map(\n lambda feature: feature.set(\n 'style', {'color': palette.get(feature.get('spectral_cluster'))}\n )\n).style(styleProperty='style')\n\n# Display the points colored by cluster ID with the S2 image.\nm = geemap.Map()\nm.set_center(-122.35, 37.47, 9)\nm.add_layer(\n image, {'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 1500}, 'S2 image'\n)\nm.add_layer(cluster_vis, None, 'Clusters')\nm\n```"]]
