Scala and Spark for Big Data Analytics

Bisecting K-means clustering of the neighborhood using Spark MLlib

In the previous section, we saw how to cluster similar houses together to determine the neighborhood. The bisecting K-means is also similar to regular K-means except that the model training that takes different training parameters as follows:

// Cluster the data into two classes using KMeans val bkm = new BisectingKMeans()                  .setK(5) // Number of clusters of the similar houses                 .setMaxIterations(20)// Number of max iteration                 .setSeed(12345) // Setting seed to disallow randomness val model = bkm.run(landRDD)

You should refer to the previous example and just reuse the previous steps to get the trained data. Now let's evaluate clustering by computing WSSSE as follows:

val WCSSS ...