Your friend owns a computer store in Yuen Long, selling Desktop and Notebook PCs and other computer peripherals. Having been rather successful with his business there, he decided to venture into the infamous Mongkok Computer Center and has already been there for three months. As expected, compared to his Yuen Long store, his new store has been recording much larger revenue but when it comes to profit, he is not so sure. He needs to pay several times more in rent! In order to stimulate sales, your friend feels that he needs to understand his customers in Mongkok more. To help him do so, you have asked for a sample of the transactional data he collected and they are shown in Table.
a) Set the Minimum Support to 18% and Minimum Confidence to 80%, find all frequent large itemsets (for product items) and all interesting rules using the Apriori algorithm.
(Please show your work step by step clearly and discuss what you would do with the item "Maintenance".)
b) By setting the Lift Ratio to 2, which rules you discovered in Part (a) are still interesting?
c) How many possible association rules (even though both the support and confidence are 0) would be generated from the following itemsets: {Case, Desktop, Maintenance, Mouse, Speaker, Webcam} and {Computer, Printer, Peripherals, Notebook_PC}. Compare the results, what you can conclude?