"Clustering models"

Ozone

Okay, so it is not a smart solution to read out the model file, convert it and to read in the tree structure!

What I want to find out is:

If there are some structures (e.g. rules: if...then...else....) in my datasets ( RapidMiner detects for me, hopefully ),

how can i figure out if different datasets have the same structures ( in sense of the rules, not only the data )???

What do you think about this option:

1. I learn a model ( e.g. a decision tree ) for every dataset and apply it to all other datasets.

Then I will check the performance for every model and if the performance is almost equal to the reference performance, the rules are similar ( if there is an unambiguous solution )

Fabian_Wewers

Hi Ozone,

why did you start a new thread?
Why is the awchisholm's solution not practible?
I don't get the point whether you only want to find duplicates of whole rules (even here the export to a file works) or want to compare each tree element to each other.
Another question: Do you only have rules with the depth of one ore branched/combined rules as well?

There are you few points you have to pay attention to with your new strategy:

• You can receive the same performance result of different models on the same dataset, so it might be sufficient for a heuristic
• The performance of a classification can be measured in different ways, so there might be different results for different models on the same datasets

Hoping my comment supports you, greetings


Fabian