---
title: K-means
description: "K-means为ML API。"
url: https://www.hikunpeng.com/document/detail/zh/kunpengboostkithistory/2200/bds/kunpengbdssparkml_16_0018.html
sourcePath: /source/zh/kunpengboostkithistory/2200/bds/kunpengbdssparkml_16_0018.html
indexId: 1bad0b1bd61e1b018c6215e1642268df3013053b310e9cf4adeae9efb7388e1073
---
# K-means

K-means为ML API。

| 模型接口类别 | 函数接口 |
| --- | --- |
| ML API | def fit(dataset: Dataset[\_]): KMeansModel |
| ML API | def fit(dataset: Dataset[\_], paramMaps: Array[ParamMap]): Seq[KMeansModel] |
| ML API | def fit(dataset: Dataset[\_], paramMap: ParamMap): KMeansModel |
| ML API | def fit(dataset: Dataset[\_], firstParamPair: ParamPair[\_], otherParamPairs: ParamPair[\_]\*): KMeansModel |


#### ML API

- 功能描述
  传入Dataset格式的样本数据，调用fit接口，输出KMeans聚类模型。


- 输入输出

  1. 包名：package org.apache.spark.ml.clustering
  2. 类名：KMeans
  3. 方法名：fit
  4. 输入：Dataset[_]，训练样本数据，必须字段如下| 参数名称 | 取值类型 | 缺省值 | 描述 |
| --- | --- | --- | --- |
| featuresCol | Vector | features | 特征标签 |


  5. 基于原生算法优化的参数

```
def setFeaturesCol(value: String): KMeans.this.type
def setPredictionCol(value: String): KMeans.this.type
def setK(value: Int): KMeans.this.type
def setInitMode(value: String): KMeans.this.type
def setInitSteps(value: Int): KMeans.this.type
def setMaxIter(value: Int): KMeans.this.type
def setThreshold(value: Double): KMeans.this.type
def setTol(value: Double): KMeans.this.type
def setSeed(value: Long): KMeans.this.type
```


  6. 新增算法参数| 参数名称 | 参数含义 | 取值类型 |
| --- | --- | --- |
| sampleRate | 每一轮迭代使用的数据占全量数据集的比例 | 0~1[Double] |
| optMethod | 样本数据采样触发开关 | default/allData[String] |


    参数及fit代码接口示例： 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 import org.apache.spark.ml.param.{ParamMap, ParamPair} val kmeans = new MlKMeans() //定义def fit(dataset: Dataset[_], paramMap: ParamMap) 接口参数 val paramMap = ParamMap(kmeans.initSteps -> initSteps) .put(k-means.maxIter, maxIter) // 定义def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): 接口参数 val paramMaps: Array[ParamMap] = new Array[ParamMap](2) for (i <- 0 to 2) { paramMaps(i) = ParamMap(kmeans.initSteps -> initSteps) .put(kmeans.maxIter, maxIter) }//对paramMaps进行赋值 // 定义def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*) 接口参数 val initStepsParamPair = ParamPair(kmeans.initSteps, initSteps) val maxIterParamPair = ParamPair(kmeans.maxIter, maxIter) val tolParamPair = ParamPair(kmeans.tol, tol) // 调用各个fit接口 model = kmeans.fit(trainingData) model = kmeans.fit(trainingData, paramMap) models = kmeans.fit(trainingData, paramMaps) model = kmeans.fit(trainingData, initStepsParamPair, maxIterParamPair, tolParamPair)

  7. 输出：KMeansModel，KMeans聚类模型，模型预测时的输出字段如下| 参数名称 | 取值类型 | 缺省值 | 描述 |
| --- | --- | --- | --- |
| predictionCol | Int | prediction | predictionCol |


- 使用样例
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 import org.apache.spark.ml.clustering.KMeans import org.apache.spark.ml.evaluation.ClusteringEvaluator // Loads data. val dataset = spark.read.format("libsvm").load("data/mllib/sample_kmeans_data.txt") // Trains a k-means model. val kmeans = new KMeans().setK(2).setSeed(1L) val model = kmeans.fit(dataset) // Make predictions val predictions = model.transform(dataset) // Evaluate clustering by computing Silhouette score val evaluator = new ClusteringEvaluator() val silhouette = evaluator.evaluate(predictions) println(s"Silhouette with squared euclidean distance = $silhouette") // Shows the result. println("Cluster Centers: ") model.clusterCenters.foreach(println)