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# evaluattion EMclustering

nguyenxuanhau
Member Posts:

**22**Maven
I run EMClustering Op with initial parameter K=30 (number clusters) but after the result has 30 cluster in which have 18 clusters no data. Why is it? (in theory, each cluster must has one data object at least)

Help me!

Thanks

Help me!

Thanks

0

## Answers

2,531Unicornwhich version of RapidMiner do you use? I remember there had been a problem in the past with this clustering algorithm.

If it's the most current version of RapidMiner, please post a bug report on bugs.rapid-i.com. If possible with the process and the input data.

Greetings,

Sebastian

22Maven- I run EMClustering Op with initial parameter K=30 (number clusters) but after the result has 30 cluster in which have 18 clusters no data. Why is it? (in theory, each cluster must has one data object at least)

and the reuslt of Kernel Kmean Clustering all so.

Help Me

Thanks

1,963RM Engineeringyour RapidMiner version is the problem. I would strongly suggest you update RapidMiner to the latest version 5.1 to fix your problem.

Regards,

Marco

22MavenRapidminer 4.6 has tutorial that says to expand and write new operator but I readed Rapididminer tutorial 5.1don't say to expand and write new operator.

Do you have any material that say to expand and write new operator in Rapidminer 5.1?

Hlep me

Thanks

22MavenWhy is it?

Help me

thanks

1,751RM Founderthis is explained in our white paper:

http://rapid-i.com/component/page,shop.product_details/flypage,flypage.tpl/product_id,52/category_id,5/option,com_virtuemart/Itemid,180/

Cheers,

Ingo

1,751RM Founderabout the empty clusters: well, I don't see a real problem with empty clusters in EM clustering. The clusterer starts with a set of random distributions and assigns points to those distributions. At the end, you will get a lot of cluster probabilities in the data set but it might of course easily be the case that the hard decision (which cluster is the one with the highest probability) will favor some clusters more than others. This is often the case if you have defined a too high number of clusters.

Here is a simple process showing the cluster model and clustered data set Iris for k = 30: Cheers,

Ingo

22MavenSo, Why did the result of W-EM clustering with k=30 (or k>30) that don't have empty clusters but The result of EMClustering had some empty clusters. (W-EM and EM are implementations of the same algorithm)

I thought that the two results were often must different little but they were different all

Why is it?

Help me

Thanks