1. Market Basket Analysis
Problem: given a database of transactions of customers of a supermarket, find the set of frequent items co-purchased and analyze the association rules that is possible to derive from the frequent patterns and how their rankings vary for different relevance measures (confidence, lift, etc.).
Input:
supermarket.arff -- to be used with nodes: Association Rule Learner, Association Rule Learner (Borgelt), and Item Set Finder (Borgelt)
supermarket_weka.arff – to be used with node FPGrowth

2. Learning curve
Problem: Show experimentally whether the following statement is true or false:
for a fixed test set of 1000 rows, the larger is the training set the more accurate is the classier.
Input: census.arff

3. Customer Segmentation
Problem: given the dataset of RFM (Recency, Frequency and Monetary value) measurements of a set of customers of a supermarket, find a high-quality clustering using K-means and discuss the profile of each found cluster (in terms of the purchasing behavior of the customers of each cluster).
Input: rfm.arff
Recency = no. of days since last purchase
Frequency = no. of distinct shopping days
Monetary = total amount spent in purchases