What is the relation between cluster size and centroid table? Which model makes more sense? Why?
NatalySimth
Member Posts: 8 Contributor II
in Help
Hello folks,
I am working on comparing two results and I have them as below:
My question is : What is the relation between cluster size and centroid table? Which model makes more sense? Why?
(Case1):
(Case 2):
I am working on comparing two results and I have them as below:
My question is : What is the relation between cluster size and centroid table? Which model makes more sense? Why?
(Case1):
(Case 2):
Tagged:
2
Best Answer

lionelderkrikor Moderator, RapidMiner Certified Analyst, Member Posts: 1,194 UnicornHi @NatalySimth,
1. By "compacity" I mean "how close the data points are from their centroid".
2.Create performance and Elbow :
You can easily create a such curve by using an optimization loop via the Optimize parameters and Cluster Distance Performance operators.
By executing a such process, you will obtain a table of the Average within Centroid Distance according to k (the number of clusters) :
Then you can plot this table with a Series type plot with :
 Index Dimension = k
 Plot Series = Average within centroid distance.
You obtain the following curve :
For this example, we can find that the inflexion point (Elbow) is for k = 4 or k = 5. Thus the optimal number clusters k for this use case is k = 4 or k =5 .
The process used for this post is in attached file.
Hope this helps,
Regards,
Lionel
PS : To understand the concept of clustering you can visit the RapidMiner Academy : There are interesting videos on this topic :
https://academy.rapidminer.com/catalog?query=cluster
9
Answers
Without any additionnal informations, to have a general idea, you can calculate the Average within centroid distance which measure the "compacity" of the clusters.(to compare the 2 models).
For that, you have to put a Performance (Cluster Distance Performance) operator at the end of your process.
Edit :
I wanted to correct /complete the explanation above :
Assuming that you are using Kmeans algorithm, a method to find the best k (number of cluster(s)), and thus the best model, is to plot the "Average within centroid distance" according to "k". You will obtain a curve like that (or in the opposite direction since the Average within centroid distance are negative in RapidMiner):
The best k and thus the more relevant model matches with the inflexion point of the curve.
Hope this helps,
Regards,
Lionel
how can I create performance and Elbow? still new to all of these methods.
You're welcome !
Regards,
Lionel
thank you for your inspiring answer from above! In this sense, it should be also possible to generate the Ellbow by using the DaviesBouldin index in order to compare the main criterion, right?
Thank you in advance for your answer!
Regards!
But can you please let me know how you get the inertia plot in rapidminer, as the options present in it are only avg within centroid and DB.
I want to plot it on the basis of inertia criterion. Please help