Frequent itemset mining leads to the discovery of association and correlations among items in huge transactional or relational data sets. The discovery of interesting correlation relationships among huge amounts of business transaction records can help in many business decision making process, such as catalog design, cross-marketing, and customer shopping behavior analysis

A typical example of frequent itemset mining is market basket analysis. This process analyzes customer buying habits by finding associations between the different items that customers place in their shopping carts. The discovery of such associations can help retailers develop marketing strategies by gaining insight into which items are frequently purchased together by customers. For example, if customers are buying milk, how likely they also buy eggs on the same visit to the supermarket? Such information can lead to increased sales by helping retailers do selective marketing and plan their shelf place

How Market Basket Analysis can be useful?

Suppose, as manager of Bizcart4all branch, you would like to learn more about the buying habits of your customers. Specifically, you want to know, " Which groups or sets of items are customers are likely to purchase on a given visit to the store?". To answer this question, market basket analysis may be performed on the retail data of customer transaction at your store.

You can then use the results

1. In the design of a new catalog
2. In the design of shlef location
3. In devicing the advertising and marketing strategy

For example, market basket analysis may help you design different store layouts

In one strategy, items that are frequently purchased together can be placed in proximity in order to further encourage the sale of such items together. If customers who purchase Milk also tend to buy Bread at the same time then placing the Milk Shelf close to the Bread Shelf may help increase the sales of both items.

In an alternative strategy, placing Milk and Bread at opposite ends of the store may entice customers who purchase such items to pick up other items along the way. For example, after buying Milk the customer may intend to buy tea bags , after that sugar cubes etc. Market basket analysis can also help retailers plan which items to put on sale at reduced prices.

In anohter example, if customers tend to purchase computers and printers together, then having the sale of printers may encourage the sale of printers as well as computers.

Data Science?<< Previous
Next >>Association Rules

Reference: Data Mining Concepts and Techniques by Jiawei Han and Micheline Kambe

Our aim is to provide information to the knowledge