In this video, the presenter explains how to apply the Apriori algorithm to generate strong association rules for a given dataset. The dataset consists of five transaction ids where customers buy various products like bread, butter, milk, beer, cookies, and diapers. The minimum support and confidence for generating association rules are set at 40% and 70% respectively. The process involves generating frequent item sets based on support counts. The presenter illustrates step by step how to calculate support counts for each product in the dataset and identify frequent item sets. Through calculations and explanations, it is shown how to determine the minimum support count needed to consider an item set as frequent. In the example provided, all items except cookies meet the minimum support criteria to be considered as frequent item sets.