kmeans

K均值（K-means）算法

).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)

spark.stop()
}
}

运行结果：

18/10/23 15:41:31 INFO BlockManagerInfo: Removed broadcast_25_piece0 on 10.200.78.114:60410 in memory (size: 519.0 B, free: 1992.8 MB)

Silhouette with squared euclidean distance = 0.9997530305375207

Cluster Centers: 

[0.1,0.1,0.1]

[9.1,9.1,9.1]