Association Rule mining is a powerful tool in Data Mining. In large databases, it is used to identifying correlation or pattern between units. Market Basket analysis is one of the ways to derive associations by examining the buying habits of the customers in their baskets. Market Basket Analysis is a mathematical modeling technique based upon the theory that if you buy a certain group of items, you are likely to buy another group of items. It is used to analyze the customer purchasing behavior and helps in increasing the sales and maintain inventory by focusing on the point of sale transaction data.

Particularly you look for combinations of products that frequently co-occur in transactions. This is done by identifying the frequent items purchased by the customers. For example, maybe people who buy pasta and cheese, also tend to buy olive oil (because a high proportion of them are planning on cooking pasta). A retailer can use this information to inform:

  • Store layout (put products that co-occur together close to one another, to improve the customer shopping experience)
  • Marketing (e.g. target customers who buy pasta with offers on olive oil, to encourage them to spend more on their shopping basket)

Online retailers and publishers can use this type of analysis to:

  • Inform the placement of content items on their media sites, or products in their catalogue
  • Drive recommendation engines
  • Deliver targeted marketing (e.g. emailing customers who bought products specific products with other products and offers on those products that are likely to be interesting to them.)

Although Market Basket Analysis is most often used to derive shoppers insights or draws a picture of supermarket in our minds, it is important to realize that there are many other areas in which it can be applied. These include:

  • Analysis of credit card purchases.
  • Analysis of telephone calling patterns.
  • Identification of fraudulent medical insurance claims.
    (Consider cases where common rules are broken).
  • Analysis of telecom service purchases.

Business use of market basket analysis has significantly increased since the introduction of electronic point of saleAmazon uses affinity analysis for cross-selling when it recommends products to people based on their purchase history and the purchase history of other people who bought the same item.

Some Basic Definitions associated with Market Basket Analysis are as follows –

  • Transaction is a set of items (Itemset).
  • Confidence : It is the measure of uncertainty or trust worthiness associated with each discovered pattern.
  • Support : It is the measure of how often the collection of items in an association occur together as percentage of all transactions
  • Frequent itemset : If an itemset satisfies minimum support,then it is a frequent itemset.
  • Strong Association rules: Rules that satisfy both a minimum support threshold and a minimum confidence threshold
  • In Association rule mining, we first find all frequent itemsets and then generate strong association rules from the frequent itemsets

Given a dataset, the Apriori Algorithm trains and identifies product baskets and product association rules. Apriori algorithm is the most established algorithm for finding frequent item sets mining. The basic principle of Apriori is “Any subset of a frequent itemset must be frequent”. We use these frequent itemsets to generate association rules.

Related Articles:

How store-based retailers are moving from general propositions to applying rules to individual customers

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