In this chapter, we learned about a specific type of recommender engine, under the umbrella term market basket analysis.
We saw that market basket analysis enabled you to mine large quantities of transactions containing semi-structured data to derive association rules among the itemsets contained in each basket.
Some additional data cleaning techniques were used on the market basket data, in order to standardize and consolidate some of the descriptions of the purchased items. We also learned how to isolate the most powerful rules, using plotting techniques, along with metrics such as lift, support, and confidence.
Finally, we showed you how to generate clusters from your market basket data training data, and to predict cluster assignments ...