# When thinking about the association rule, answer the following questions this week.

When thinking about the association rule, answer the following questions this week.

1. What is the association rule in data mining?
1. Why is the association rule especially important in big data analysis?
1. How does the association rule allow for more advanced data interpretation?

Requirements:

• Students must not copy and post from sources.  When referencing sources, students must rephrase all work from author’s and include in-text citations and references in APA format.

USEFUL NOTES FOR:

What is the association rule in data mining?

Introduction

Data mining is a field of computer science that applies mathematical techniques to data in order to extract useful information from them. It involves finding patterns in large datasets by using mathematical models and algorithms.

An association rule is defined as an implication expression of the form X → Y where X and Y are disjoint itemsets.

An association rule is defined as an implication expression of the form X → Y where X and Y are disjoint itemsets. In other words, if there are two or more items that occur together more often than expected by chance, then we consider them to be associated with each other.

The rule X → Y holds true in a dataset, if the proportion of the transactions that contain both X and Y, is greater than the user-specified minimum threshold support, i.e., minsupport, and the proportion of transactions in which Y occurs given that X occurs is greater than user-specified minimum confidence threshold, i.e., minconfidence.

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For example “The probability of customer purchases milk with bread is 80%” can be written as {milk}→{bread}. Here, buying milk and bread together has 80% support in customer transaction data. This {milk}→{bread} association rule has a support of 80% and a confidence of 100%.

Support and confidence are two measures of the association rule.

Support is the proportion of transactions that contain both X and Y. Confidence is the proportion of transactions in which Y occurs given that X occurs. For example, if we have a sample with 100 records and \$20 purchases from customers who bought bread and milk together, then we can calculate support for this association rule as follows:

Let’s understand the terms Support (80%) and Confidence (100%) using another example. If a survey reveals that 60% people like to eat Apple Pie for dessert after having dinner then the support for {ApplePie}→{Dinner} can be stated as 60%. If this data further reveals that 75% people who have dinner likes to have Apple Pie for dessert then the confidence can be stated as 75%.

Let’s understand the terms Support (80%) and Confidence (100%) using another example. If a survey reveals that 60% people like to eat Apple Pie for dessert after having dinner then the support for {ApplePie}→{Dinner} can be stated as 60%. If this data further reveals that 75% people who have dinner likes to have Apple Pie for dessert then the confidence can be stated as 75%.

Support is calculated by finding out how many transactions contain both X and Y. Confidence is calculated by finding out how many transactions occur in which Y occurs given that X occurs. The support or confidence is determined based on a threshold value, if the support or confidence falls below this threshold then we say that rule does not have enough support/confidence

Conclusion

So, association rules are formed by the input data and they are used to predict new instances of a target category. So if I have a dataset of transactions in which I want to predict whether or not someone bought milk with bread then I will create two sets of itemsets as follows:

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