As far as I know the limits are quite large, if you have less than 2^31 rows (and a lot of memory/time when you have lot of data) you will be safe.
I am not sure I understand your second question... Any string can be in the class column even numbers. You might need to make it a nominal column (compute its domain with Domain Calculator node). But if you are asking whether the NN nodes can be used for clustering not just classification, I do not know any options to do that.
i wantto use PNN (probabilistic neural network) for classification in KNIME. I have 59 feature or variable. But only 15 of them are continues and the others are categorical. Can i use categorical variable in PNN? If not, what should i do?
The PNN Learner works only on numeric columns and ignores categorical attributes. You can use the One2Many node to convert those columns into numeric (bitvector) columns and use them as input for the learner.
thanky you for your reply about PNN. I have anothre question: for using PNN, shoule i normalized variables? or PNN node have the algorithm for normalization? if i should normalyzed the variables, which node is apropriate?
The so-called Normailzer node has different options for normalizing numeric data. However, if you have used to One2Many node you will end up with bits (0, 1) which don't need to be normalized anymore...
thank you very much for your reply. i have another question . i understand that with using one 2 many node there is not need for normalizing categorical data. should i use normalizer for numeric data for PNN?
For the PNN, the data does not need to be normalized. On the other hand, the Rprop (MLP) should have the data normalized; within the configuration you can adjust the number of hidden neurons; the number of input and output neurons is defined by the data (input dimensions and number of classes). I also suggest looking into the Weka extension for KNIME for more like state-of-the-art algorithms.