Hi,
For machine learning centroid cluster algorithm, we often use is
Calinsk-iHarabasz score to evaluate which algorithm or how many centers is best
for a dataset.
The python lib sklearn implements Calinsk-iHarabasz as
sklearn.metrics.calinski_harabasz_score.
I think there should be a CalinskiHarabaszClusterEvaluator in commons math:
```java
package org.apache.commons.math4.ml.clustering.evaluation;
import org.apache.commons.math4.ml.clustering.Cluster;
import org.apache.commons.math4.ml.clustering.Clusterable;
import java.util.List;
public class CalinskiHarabaszClusterEvaluator<T extends Clusterable> extends
ClusterEvaluator<T> {
@Override
public double score(List<? extends Cluster<T>> clusters) {
//TODO: Implement the Calinski-Harabasz Score algorithm
return 0;
}
@Override
public boolean isBetterScore(double score1, double score2) {
return score1 > score2;
}
}
```
The code can be implemented by read the algorithm documents,
or translate from python sklearn.metrics.calinski_harabasz_score.
