Stay organized with collectionsSave and categorize content based on your preferences.
X-Means is K-Means with an efficient estimation of the number of clusters. For more information see:
Dan Pelleg, Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727-734, 2000.
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-07-13 UTC."],[[["\u003cp\u003eX-Means extends the K-Means clustering algorithm by efficiently estimating the optimal number of clusters within a specified range.\u003c/p\u003e\n"],["\u003cp\u003eThe algorithm iteratively evaluates potential cluster splits using a Bayesian Information Criterion (BIC) to determine the most likely number of clusters.\u003c/p\u003e\n"],["\u003cp\u003eUsers can customize parameters like the minimum and maximum number of clusters, iterations, distance function, and randomization seed for fine-grained control over the clustering process.\u003c/p\u003e\n"],["\u003cp\u003eImplemented within Earth Engine, X-Means offers a scalable solution for geospatial data analysis and pattern recognition tasks.\u003c/p\u003e\n"]]],[],null,["# ee.Clusterer.wekaXMeans\n\nX-Means is K-Means with an efficient estimation of the number of clusters. For more information see:\n\n\u003cbr /\u003e\n\nDan Pelleg, Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727-734, 2000.\n\n| Usage | Returns |\n|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------|\n| `ee.Clusterer.wekaXMeans(`*minClusters* `, `*maxClusters* `, `*maxIterations* `, `*maxKMeans* `, `*maxForChildren* `, `*useKD* `, `*cutoffFactor* `, `*distanceFunction* `, `*seed*`)` | Clusterer |\n\n| Argument | Type | Details |\n|--------------------|------------------------------|------------------------------------------------------------------------------------|\n| `minClusters` | Integer, default: 2 | Minimum number of clusters. |\n| `maxClusters` | Integer, default: 8 | Maximum number of clusters. |\n| `maxIterations` | Integer, default: 3 | Maximum number of overall iterations. |\n| `maxKMeans` | Integer, default: 1000 | The maximum number of iterations to perform in KMeans. |\n| `maxForChildren` | Integer, default: 1000 | The maximum number of iterations in KMeans that is performed on the child centers. |\n| `useKD` | Boolean, default: false | Use a KDTree. |\n| `cutoffFactor` | Float, default: 0 | Takes the given percentage of the split centroids if none of the children win. |\n| `distanceFunction` | String, default: \"Euclidean\" | Distance function to use. Options are: Chebyshev, Euclidean, and Manhattan. |\n| `seed` | Integer, default: 10 | The randomization seed. |"]]
