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Hi everyone,
I wondering how I can get "prediction accuracy" for different clustering techniques in RapidMiner? Because the default performance measures such as "cluster distance performance" or "cluster density performance" do not provide prediction accuracy.
Thanks in advance,
I found the solution: "Map Clustering on Lables"
Now, I have managed to get accuracy, precision and recall for k-means ....
Thanks
Hi,
how do you define accuracy if there is no label like in usual clustering problems?
~Martin
For example, in my dataset I have 11 normal attributes and 1 lable. In classification, we get prediction accuracy based on the confusion matrix. For clustering, there is no such thing but I need a prediction performance as many papers comparing their clustering algorithms based on this measure. Fo instance, the attached image shows one of these papers which provides predicttion performance for expectation maximization and k-means.
Thaks
in usual clustering problems you don't have a label. How do you want to assign a cluster-id to a class?
This how MATLAB defines the clustering performance. MATLAB provides confusion matrix for the both classification and clustering models. Please see page 5 and 6 in the attached pdf. I need the same thing in Rapidminer environment.
Wow, good catch. That's an operator I did not come across in my last 6 years.
would it not simply be possible to use Performance ( Classification) Operator after apply model operator to see if a cluster matches a certain class in x-times of cases?
i always get this text message when im try to show my precision help please
hello @vand_boo99 welcome to the community! I'd recommend posting your XML process here (see https://youtu.be/KkgB5QXWXJ8 and "Read Before Posting" on right when you reply) and attach your dataset. This way we can replicate what you're doing and help you better.Scott
