What are the applications of Association Rule?

Association rule learning is a type of unsupervised learning methods that tests for the dependence of one data element on another data element and create appropriately so that it can be more effective. It tries to discover some interesting relations or relations among the variables of the dataset. It depends on several rules to find interesting relations between variables in the database.

The association rule learning is the important technique of machine learning, and it is employed in Market Basket analysis, Web usage mining, continuous production, etc. In market basket analysis, it is an adequate used by several big retailers to find the relations among items.

Association rules were originally transformed from point-of-sale data that represent what products are purchased together. Although its roots are in linking point-of-sale transactions, association rules can be used external the retail market to find relationships among types of “baskets.”

There are various applications of Association Rule which are as follows −

  • Items purchased on a credit card, such as rental cars and hotel rooms, support insight into the following product that customer are likely to buy.

  • Optional services purchased by tele-connection users (call waiting, call forwarding, DSL, speed call, etc.) support decide how to bundle these functions to maximize revenue.

  • Banking services used by retail users (money industry accounts, CDs, investment services, car loans, etc.) recognize users likely to needed other services.

  • Unusual group of insurance claims can be an expression of fraud and can spark higher investigation.

  • Medical patient histories can supports expressions of likely complications based on definite set of treatments.

Association rules falls to live up to expectations. For instance, they are not the best method for producing cross-selling models in market such as retail banking, because the rules end up describing previous marketing promotions. Also, in retail banking, users frequently start with a checking account and then a saving account. Differentiation among products does not occur until users have higher products.

In Apriori Algorithm, this algorithm needed frequent datasets to create association rules. It is created to work on databases that includes transactions. This algorithm needed a breadth-first search and hash tree to compute the itemset effectively.

It is generally used for market basket analysis and support to understand the products that can be purchased. It is used in the healthcare space to discover drug reactions for patients.

In Eclat algorithm, it represents Equivalence Class Transformation. This algorithm needed a depth-first search method to discover frequent itemsets in a transaction database. It implements quicker implementation than Apriori Algorithm.