spark BisectingKMeansExample 源码
spark BisectingKMeansExample 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/ml/BisectingKMeansExample.scala
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.examples.ml
// scalastyle:off println
// $example on$
import org.apache.spark.ml.clustering.BisectingKMeans
import org.apache.spark.ml.evaluation.ClusteringEvaluator
// $example off$
import org.apache.spark.sql.SparkSession
/**
* An example demonstrating bisecting k-means clustering.
* Run with
* {{{
* bin/run-example ml.BisectingKMeansExample
* }}}
*/
object BisectingKMeansExample {
def main(args: Array[String]): Unit = {
// Creates a SparkSession
val spark = SparkSession
.builder
.appName("BisectingKMeansExample")
.getOrCreate()
// $example on$
// Loads data.
val dataset = spark.read.format("libsvm").load("data/mllib/sample_kmeans_data.txt")
// Trains a bisecting k-means model.
val bkm = new BisectingKMeans().setK(2).setSeed(1)
val model = bkm.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: ")
val centers = model.clusterCenters
centers.foreach(println)
// $example off$
spark.stop()
}
}
// scalastyle:on println
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