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ee.Clusterer.wekaXMeans
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
X-Means 是 K-Means,可有效估算叢集數量。如需詳細資訊,請參閱:
Dan Pelleg、Andrew W. Moore:X-means:以有效率的叢集數估算方式擴充 K-means。In: Seventeenth International Conference on Machine Learning, 727-734, 2000.
用量 | 傳回 |
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ee.Clusterer.wekaXMeans(minClusters, maxClusters, maxIterations, maxKMeans, maxForChildren, useKD, cutoffFactor, distanceFunction, seed) | 叢集器 |
引數 | 類型 | 詳細資料 |
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minClusters | 整數,預設值為 2 | 叢集數量下限。 |
maxClusters | 整數,預設值為 8 | 叢集數量上限。 |
maxIterations | 整數,預設值為 3 | 整體疊代次數上限。 |
maxKMeans | 整數,預設值:1000 | KMeans 中要執行的疊代次數上限。 |
maxForChildren | 整數,預設值:1000 | 對子中心執行的 KMeans 疊代次數上限。 |
useKD | 布林值,預設值為 false | 使用 KDTree。 |
cutoffFactor | 浮點值,預設值為 0 | 如果沒有任何子項獲勝,則會採用指定百分比的分割重心。 |
distanceFunction | 字串,預設值為「Euclidean」 | 要使用的距離函式。選項包括:Chebyshev、Euclidean 和 Manhattan。 |
seed | 整數,預設值為 10 | 隨機化種子。 |
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上次更新時間:2025-07-26 (世界標準時間)。
[null,null,["上次更新時間:2025-07-26 (世界標準時間)。"],[[["\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. |"]]