What does each of the above cases imply?
Hii WEKA list,
I want to know if this way of classification helps.
Suppose,I have a data set, D. I first cluster D and then divide the clustered D into training (T1) and test (T2) data sets. Now, I build a classifier model on T1 and use the T2 through 'Supplied test data' in Weka. Then, I get about 93% accuracy of the classifier.
On the other hand, if I divide D into T1 and T2 before clustering and cluster T1 and T2 independently and build classifier model on T1 to find the classes for T2, I get only 4% accuracy.