This question is for frequent pattern mining algorithm Apriori and closed pattern mining algorithm like CLOSET. Implement Apriori algorithm to mine frequent pattern from a transaction dataset Implement an algorithm to mine closed frequent pattern from the same dataset. You can either write a code to extract closed patterns from the result that you got in Part 1 or code CLOSET. Input Format The input dataset is a transaction dataset. The first line of the input corresponds to the minimum support. Each following line of the input corresponds to one transaction. Items in each transaction are seperated by a space. Please refer to the sample input below. In sample input 0, the minimum support is 2, and the dataset contains 3 transactions and 5 item types (A, B, C, D and E). Constraints NA Output Format The output are the frequent patterns you mined out from the input dataset. Each line in the output should be of the format : Support [frequent pattern] Frequent patterns should be listed in a descending order based on support. e.g. 3 [C] is listed before 2 [A]. Ties should be resolved based on lexicographical order. e.g. 2 [A] is listed before 2 [A C] Items within each pattern should be listed in lexicographical order as well seperated by a single space. e.g. 2 [B C D] First print the frequent patterns and then closed pattern. Seperate the output for two parts by an empty line. In sample output 0, first 9 lines correspond to frequent patterns and last 3 lines correspond to closed pattern. Sample Input 0 2 B A C E D A C C B D Sample Output 0 3 [C] 2 [A] 2 [A C] 2 [B] 2 [B C] 2 [B C D] 2 [B D] 2 [C D] 2 [D] 3 [C] 2 [A C] 2 [B C D] Sample Input 1 2 data mining frequent pattern mining mining frequent patterns from the transaction dataset closed and maximal pattern mining Sample Output 1 4 [mining] 2 [frequent] 2 [frequent mining] 2 [mining pattern] 2 [pattern] 4 [mining] 2 [frequent mining] 2 [mining pattern] Note: The solution is expected in python 2.