Evaluate a cosine-measure Clustering by Davies Bouldin
Hi all,
I am running a process which contains a k-means operator with "cosine similarity measure". and I want to evaluate it with a "cluster distance performance" operator with "Davies Bouldin" measure. As far as I know, the Davies bouldin measure is calculated by "Eucledian distance" measure, But my cluster model has been built upon a "cosine similarity measure". SO how can I evaluate a cosine-based k-means? Any idea please?
I am running a process which contains a k-means operator with "cosine similarity measure". and I want to evaluate it with a "cluster distance performance" operator with "Davies Bouldin" measure. As far as I know, the Davies bouldin measure is calculated by "Eucledian distance" measure, But my cluster model has been built upon a "cosine similarity measure". SO how can I evaluate a cosine-based k-means? Any idea please?
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Any explanation or idea please?
Hello
I had a look at the code. I think it's this source file: CentroidBasedEvaluator.java
regards
Andrew
I had a look at the code. I think it's this source file: CentroidBasedEvaluator.java
private double getDaviesBouldin(CentroidClusterModel model, ExampleSet exampleSet) throws OperatorException {
DistanceMeasure measure = new EuclideanDistance();
I think you would have to write some Java to change how it works.regards
Andrew