Python Data item set mining

Itemset Mining with Eclat and Rule and Pattern Assessment In this assignment, you will find or generate a transaction data set and apply Eclat algorithm on this set to find frequent itemsets. Next, from the frequent itemsets list, you will choose several sets and implement seven rule and pattern assessment measures. Data: Choice of data set is up to you. You can either search on the internet or generate one. Size of data set should be big enough to make reasonable mining and assessment measures. Itemset Mining with Eclat: You can use the program provided with the assignment to automate the frequent itemset generation. Keep in mind that if you decide to use the python program provided with the assignment, your data set must be in specific format. The first element of each row in the input data file must mention the number of items in the transaction. For example, if the transaction is containing 5 items, the line of transaction can be: 5 7 2 3 5 8 Here, 5 is the number of items in the transaction and 7, 2, 3, 5, 8 are the id’s of items. Id’s of items cannot be letters or any other symbols. Examples of data sets are provided with the homework. The program takes two arguments, filename with option -f and minimum support value with option -s. It will print the frequent patterns with their tid lists, if option -p is provided. Otherwise, it will only print minimum support value and calculation time for Eclat algorithm. Rule and Pattern Assessment: After finding frequent itemsets, you will need to calculate seven measures of associations you have chosen. These measures are support, confidence, lift, leverage, Jaccard, conviction, and odds ratio. Late submissions will not be accepted. Data sets cannot be the same for two students. Good luck! Program: [login to view URL] [login to view URL] [login to view URL] [login to view URL]