Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. Step 1: Download the dataset from moodle (grocery store data.csv) and import the dataset in the rapid miner under the data folder in the local repository. Step 2: See the imported data format. Did you see any problem with the imported data? Step 3: Choose Nominal to text operator to change the type of selected attributes to text (string values). Add process Documents from the data operator to generate word vectors from string attributes. (If you dont have this operator then go to the extension tab, click on Marketplace and search for text processing extension and install it). Step 4: Open the process document from the data operator and add Tokenize, Transform Cases, and Filter Stopwords (English) operator. It will split the text into a sequence of Tokens, Transform all characters into the lower case or upper case and remove English stopwords from the document. Step 5: See the output from the Process documents from the data operator. You can see a weight assigned with each grocery item in a row. Step 6: Convert numerical data into binomial data. Step 7: Add an FP-Growth operator that efficiently calculates all the frequently occurring itemsets in the example set using FP-tree data structure. Set min support to 0.6 to change the frequent or not a frequent occurrence of an itemset to 60%. Step 8: Add create association rules operator to generate a set of association rules from the given set of a frequent itemset. Set the min confidence parameter to 0.5. Confidence is an indication of how often the rule has been found to be true. Question 1: Review the results and write down your analysis and recommendation. Task: Download the B Pipe Separated Items dataset from Moodle, apply association rules on the dataset and record your findings References McNicholas, et al., 2008. Standardising the lift of an association rule. Computational Statistics & Data Analysis, 52(10), pp.4712-4721. https://scss.tcd.ie/disciplines/statistics/tech-reports/07-01.pdf Wikipedia, https://en.wikipedia.org/wiki/Association_rule_learning