Kevin also learns frequent pattern mining algorithms. So he wants to use the above dataset as a practice. He transformed the dataset in Table 2 into the dataset in the following Table 3 Table 2. Temperatures and AC operations Index OutTem Table 3. Transaction DE Transaction ID Items 11,12,13 14, 15, 13 14, 12, 16 14, 15, 16 11,15, 13 11,12,13 14, 15, 13 11,12,13 14, 15, 13 11,12,13 AC Cool Hot Hot Hot Cool Cool Hot Cool Hot Cool InTem Cool Cool Hot Hot Cool Cool Cool Cool Cool Cool 10 10 The meaning of each item in Table 3 is shown in Table 4 Table 4. Meaning of each item tem Meaning Outdoor Temperature (F: Cool AC Running Condition: Off Indoor Temperature (F: Cool Outdoor Temperature (F: Hot AC Running Condition: On Indoor Temperature (F Hot 12 14 16 Given the dataset in Table 3, please illustrate how to mine the frequent itemsets using the Apriori algorithm. The minimum support (min sup) threshold is set to 3, in the end, please list all the frequent itemsets mined