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What is the working 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 adequate used by several big retailers to find the relations among items.
In market basket analysis, users' buying habits are analyzed by discovering associations among the different items that users place in their shopping baskets. By finding such associations, retailers create marketing approached by analyzing which components are usually purchased by users. This association can influenced to increased sales by providing retailers to do selective marketing and design for their shelf space.
The well-known area of application for the multi-level association is market basket analysis, which understands the purchasing habits of users by searching for groups of items that are frequently, purchased together which was shown in the notion of concept hierarchy.
Association rules begins with transactions including one or more products or service providing and some rudimentary data about the transaction. For the goals of analysis, the products and service providing are known as items.
These transactions have been used to contain only the items buy. It can use data like the date and time and whether the users paid with cash or a credit card.
Every transactions provides us data about which products are buy with which other products. This is displayed in a co-appearance table that tells the multiple times that some pair of products was purchased together.
These observations are an instances of associations and can suggest a formal rule like such as if a user buy soda, then the user also buy orange juice. In the data, two of the five transactions contains both soda and orange juice. These two transactions provides the rule. The support for the rule is two out of five or 40 percent.
Because both the transactions that include soda also include orange juice, there is a large degree of confidence in the rule as well. Because two of the three transactions that includes soda also includes orange juice, therefore the rule “if soda, then orange juice” has a confidence of 67 percent percent. The inverse rule, “if orange juice, then soda,” has a lower confidence.
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