Association Analysis in Data Mining

Mining for associations among items in a large database of transactions is an important data mining function.
Association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases.
Association rules are statements of the form {X1, X₂, …, Xn} => Y, meaning that if we find all of X₁, X₂, ……… , Xn in the transaction then we have good chance of finding Y. Eg: The information that a customer who buys computer also tends to buy antivirus or pen drive.
Association analysis mostly applied in the field of market basket analysis, web-based mining, intruder detection etc.

Market Basket Analysis

Market basket analysis (also known as Affinity Analysis) is the study of items that are purchased or grouped together in a single transaction or multiple, sequential transactions.
Understanding the relationships and the strength of those relationships is valuable information that can be used to make recommendations, cross-sell, up-sell, offer coupons, etc.
A predictive market basket analysis can be used to identify sets of products/services purchased/events) that generally occur in sequence or something of interest to direct marketers.
Advanced Market Basket Analysis provides an excellent way to get to know the customer and understand the different behaviors that can be leveraged to provide better assortment, design a better plan and devise more attractive promotions that can lead to more sales and profits.
The analysis can be applied in various ways:
- Develop combo offers based on products sold together.
- Organize and place associated products/categories nearby inside a store.
- Determine the layout of the catalog of an ecommerce site.
- Control inventory based on product demands and what products sell together.

Support of a product or group of products indicates the popularity of the product or group of products in the transaction set. Higher the support, more popular is the product or product bundle. This measure can help in identifying selling strategy of the store. Eg: if Barbie dolls have a higher support then they can be attractively priced to attract traffic to a store.
Confidence can be used for product placement strategy and increasing profitability. Place high-margin items with associated high selling If Market Basket Analysis indicate that customers who bought high selling Barbie dolls also bought high-margin candies, then candies should be placed near Barbie dolls.
Lift indicates the strength of an association rule over the random co-occurrence of Item A and Item B, given their individual support. Lift provides information about the change in probability of Item A in presence of Item B. Lift values greater than 1.0 indicate that transactions containing Item B tend to contain Item A more often than transactions that do not contain Item B.
In order to gain better insights, Market Basket Analysis can based on
- Weekend vs weekday sales
- Month beginning vs month-end sales
- Different seasons of the year
- Different stores
- Different customer profiles
Although Market Basket Analysis mostly applied for shopping carts and supermarket shoppers, there are many other areas in which it can be applied such as:

Analysis of credit and debit card purchases.
Analysis of cheque payments made.
Analysis of services/products taken g. a customer who has taken executive credit card is also likely to take personal loan.