Now, we'll look at how to evaluate a clustering model that has been trained. Let's look at the code and see how this is done.

We'll be using the following classes:

import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.clusterers.SimpleKMeans;
import weka.clusterers.ClusterEvaluation;

We'll use the ClusterEvaluation class from the weka.clusterers package for evaluation.

First, we will read our dataset into our DataSource object and assign it to the Instances object. Then, we'll create our k-means object and specify the number of clusters that we want to create. Next, we will train our clustering algorithm using the buildClusterer method; then, we'll print it using println. This is similar to what you saw earlier:

public static void main(String[] args) {
    // TODO code application logic here
    try{
        DataSource src = new DataSource("/Users/admin/Documents/NetBeansProjects/ClusterEval/weather.arff");
        Instances...