this rule can be read in the obvious manner that when the customer bought items on the (left hand sidelhs of the rule he is likely to buy the itemk in the later sectionswe define mathematical metrics that will capture the strength of such rules we can use these rules in variety of ways the most obvious way to use these rules is to develop bundles of products which make it convenient for the customer to buy these items together another way to use these rules is to bundle products along with some discounts for other relevant products in the bundlehence ensuring that the customer becomes more likely to buy more items some unlikely ways to use association rules is for designing better web site navigational structureintrusion detectionbioinformaticsand so on association rule-mining basics before proceeding to explore the association rules in our datasetwe will go through some essential concepts for association rule-mining these terms and concepts will help you in the later analysis and also in understanding the rules that the algorithm will generate consider table - with some toy transaction data table - example of transaction set trans id items {milkbread {butter {beerdiaper {milkbreadbutter {breadeach row in the table consists of transaction for examplethe customer bought milk and bread in the first transaction following are some vital concepts pertaining to association rule-mining itemsetitemset is just collection of one or more items that occur together in transaction for examplehere {milkbreadis example of an itemset supportsupport is defined as number of times an itemset appears in the dataset mathematically it is defined assupp ({beer diaper}number of transactions with beer and diaper total transactions in the previous examplesupport (beerdiaper / confidenceconfidence is measure of the times the number of times rule is found to exist in the dataset for rule which states {beer (rdiaperthe confidence is mathematically definedasconfidence ({beer diaper} supp beer and diaper supp beer