Write a minimum of 2.5 page paper that describes the basic concepts of Association Analysis. Also describe the market basket analysis with examples. Use Times 12 font, double space, with 1 inch margin. In addition to the 2.5 pages write-up, add answers to the following to your paper. For each of the following questions, provide an example of an association rule from the market basket domain that satisfies the following conditions. Also, describe whether such rules are subjectively interesting. Your answers should have detailed responses, failing which several points will be deducted. (a) A rule that has high support and high confidence. (b) A rule that has reasonably high support but low confidence. (c) A rule that has low support and low confidence. (d) A rule that has low support and high confidence. Note-1: for citations, use the following format: https://owl.purdue.edu/owl/research_and_citation/resources.html If any work is not cited or plagiarized, student will receive zero points for the Research paper. Note-2: If instructions of font size, margin and paper length are not followed, several points will be deducted.

Association analysis, also known as market basket analysis, is a data mining technique used to discover relationships between items in a dataset. It is based on the observation that certain items are frequently purchased or consumed together, and by identifying these associations, businesses can gain insights into customer behavior and make informed marketing decisions.

The main objective of association analysis is to uncover meaningful associations or rules between items in a transaction dataset. These rules are typically expressed as “if-then” statements, where the antecedent represents a set of items that imply the presence or absence of another item, the consequent. The strength of an association rule is measured by two key metrics: support and confidence.

Support is defined as the proportion of transactions in the dataset that contain both the antecedent and the consequent. High support values indicate that the rule is applicable to a large number of transactions and is considered as a strong rule.

Confidence, on the other hand, measures the reliability of the rule. It is defined as the proportion of transactions containing the antecedent that also contain the consequent. High confidence values suggest that the rule is accurate and reliable.

Now, let’s consider some examples of association rules in the market basket domain that satisfy different conditions:

(a) A rule that has high support and high confidence:
“If a customer buys bread and milk, then they are likely to buy butter.”
This rule has high support because a large number of customers buy bread, milk, and butter together. It also has high confidence because the majority of customers who buy bread and milk also buy butter. Such rules are subjectively interesting because they indicate strong associations between items that can be used for cross-selling or targeted marketing campaigns.

(b) A rule that has reasonably high support but low confidence:
“If a customer buys fruits, then they are likely to buy vegetables.”
This rule has reasonably high support because many customers buy fruits and vegetables together. However, it has low confidence because only a subset of customers who buy fruits also buy vegetables. Such rules may still be considered interesting, but they may require further investigation or additional factors to improve their reliability.

(c) A rule that has low support and low confidence:
“If a customer buys a specific brand of chips, then they are likely to buy a specific brand of soda.”
This rule has low support and low confidence because only a small number of customers buy both the specific brand of chips and soda together. Such rules may not be subjectively interesting as they represent weak associations that may not have significant implications for marketing strategies.

(d) A rule that has low support and high confidence:
“If a customer buys a luxury handbag, then they are likely to buy a matching wallet.”
This rule has low support because only a few customers buy luxury handbags. However, it has high confidence because the majority of customers who buy luxury handbags also buy matching wallets. Such rules can be subjectively interesting for luxury brands as they indicate opportunities for upselling or bundling products.

In conclusion, association analysis is a powerful technique for discovering relationships between items in a transaction dataset. The support and confidence metrics help evaluate the strength and reliability of association rules. By analyzing these rules, businesses can gain valuable insights into customer behavior and make informed marketing decisions.

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