You may restrict your experiments to a subset of the dataset IF Weka cannot handle your whole dataset (this is unlikely). But remember that the more accurate your system is, the better.
A main part of this project is the PREPROCESSING of your dataset. You must apply relevant concept hierarchies and generalizations to your dataset before doing the mining and/or using the results of previous mining tasks (e.g. project 2, initial minings of the data using ID3). Your report should contained a detailed description of the preprocessing of your dataset and justifications of the steps you followed. If Weka does not provide the functionality you need to preprocess your data as you need to obtain useful patterns, preprocess the data yourself either by writing the necessary filters (you can incorporate them in Weka if you wish).
Use n-fold crossvalidation to test your results. I recommend using n=4, but you may use a different value IF needed given your dataset (you'll need to justify a different selection in your report).
Your report should contain the following sections with the corresponding discussions:
Provide a detail description of the preprocessing of your data. Justify the preprocessing you apply and why the resulting data is the appropriate one for mining decision trees from it.