Bisecting k-means
Bisecting k-means is a kind of hierarchical clustering using a divisive (or β€œtop-down”) approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
Bisecting K-means can often be much faster than regular K-means, but it will generally produce a different clustering.
BisectingKMeans is implemented as an Estimator and generates a BisectingKMeansModel as the base model
1
import org.apache.spark.ml.clustering.BisectingKMeans
2
// Loads data.
3
val dataset = spark.read.format("libsvm").load("file:///opt/spark/data/mllib/sample_libsvm_data.txt")
4
// Trains a bisecting k-means model.
5
val bkm = new BisectingKMeans().setK(2).setSeed(1)
6
val model = bkm.fit(dataset)
7
// Evaluate clustering.
8
val cost = model.computeCost(dataset)
9
println(s"Within Set Sum of Squared Errors = $cost")
10
// Shows the result.
11
println("Cluster Centers: ")
12
val centers = model.clusterCenters
13
centers.foreach(println)
14
​
15
/*
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Output:Within Set Sum of Squared Errors = 2.1480729824555594E8
17
Cluster Centers:
18
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