Write short note on Market Basket Analysis.

Write short note on Market Basket Analysis.
1.     Frequent itemset mining leads to the discovery of associations and correlations among items in large 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 processes, such as catalog design, cross-marketing, and customer shopping behavior analysis.
2.     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 baskets” as shown in the figure.
1.     The discovery of such associations can help retailers develop marketing strategies by gaining insight into which items are frequently purchased together by customers.
2.     For instance, if customers are buying milk, how likely are they to also buy bread (and what kind of bread) on the same trip to the supermarket? Such information can lead to increased sales by helping retailers do selective marketing and plan their shelf space.
3.     Example: Suppose, as manager of a store, one would like to learn more about the buying habits of the customers like which groups or sets of items are customers likely to purchase on a given trip to the store.
4.     Market basket analysis may be performed on the retail data of customer transactions at the store. The results can be used to plan marketing or advertising strategies, or in the design of a new catalog.
5.     For instance, market basket analysis may help design different store layouts.
6.     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.
7.     In an alternative strategy, placing hardware and software at opposite ends of the store may entice customers who purchase such items to pick up other items along the way.
8.     Market basket analysis can also help retailers plan which items to put on sale at reduced prices.
9.     Each item in the store has a Boolean variable representing the presence or absence of that item. Each basket can then be represented by a Boolean vector of values assigned to these variables.
10.  The Boolean vectors can be analyzed for buying patterns that reflect items that are frequently associated or purchased together. These patterns can be represented in the form of association rules.


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