Can we use clustering output as predictor variable for classification? Can we use clustering output as predictor variable for classification?


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*I have a set of data and I do clustering analysis on it, it divides the data into different clusters.

*Can I use this cluster information i.e. cluster1, cluster2, cluster3, as one of the input variable i.e. predictor variable for my decision tree algorithm
is it statistically OK?
I found great increase in the model accuracy when I do this

 A: You will face the problem how to assign new instances to their clusters.
The more common combination is to run cluster analysis to check if any class consists maybe of multiple clusters. Then use this information to train multiple classifiers for such classes (i.e. Class1A, Class1B, Class1C), and in the end strip the cluster information from the output (i.e. Class1A -> Class1).
If you are running cluster analysis first, then split your data, you have a data snooping bias problem. Don't do this. Running clusterig on the train and test set independently will usually not work, as clusters will often be very different.
In some rare cases (e.g. k-means) you can of course assign new instances to the nearest mean, with the means optimized on the training set only. But this is in fact no longer k-means, but NN-classification to a simplified data set (consisting of the means obtained by k-means). This approach is however only possible for the particular case of k-means with squared Euclidean distance.
A: As @Peter Flom already stated, Yes, you can do this; however, keep in mind that you are sacrificing interpretation for prediction in this case (ie. what do changes in these clusters truly mean?). This may or may not be important to the yourself and/or the end user of your model. 
If prediction (model accuracy, in your case) is the only thing that is important to you in this situation then by all means use the clustered variables as inputs in your model!
A: Yes, you can do that. Why would you not be able to?
