Graded Assignment:  Association Analysis You work for a hypothetical university as an entry level data analyst and your supervisor has task you to learn more about the data mining process associated with modeling more specifically using association analysis following the steps below. Important Reminder:  Assessment of written assignments account for 30% of overall grading and below is a breakdown of how I will assess grading for this assignment: Assessment Criteria Possible Points Points Earned Student included a front APA cover page (Page 1) 5 Student included an abstract (Page 2) 5 Student included a minimum of three body pages of content supported with three academic sources of research addressing methods and approaches in using association analysis. 60 Student included data visualizations/illustrations to correlate or support written content. 15 Student included in-text citations with a complete reference page properly formatted using APA 10 Student included a completed paper free of grammar and spelling issues. 5 Total Earned points 100 Comments:

Association analysis is a data mining technique used to discover relationships or associations between items in a large dataset. It is commonly used in various industries, such as retail, marketing, and healthcare, to understand patterns and make informed decisions. In this assignment, we will explore the steps involved in association analysis and its significance in modeling.

The first step in association analysis is data preparation. This involves collecting and cleaning the data to ensure its quality and consistency. It is important to identify the variables of interest and convert the data into a suitable format, such as a transactional format or binary format. This step is crucial as the quality of the data directly affects the accuracy and reliability of the association rules generated.

The next step is identifying frequent itemsets. An itemset refers to a collection of items that frequently co-occur in the dataset. In association analysis, frequent itemsets are identified based on a minimum support threshold. The support of an itemset is defined as the percentage of transactions containing that itemset. Itemsets that meet the minimum support threshold are considered frequent itemsets and are used in the subsequent steps.

Once frequent itemsets are identified, the next step is generating association rules. An association rule consists of an antecedent (a set of items) and a consequent (a single item). These rules indicate the probability of occurrence of the consequent item given the antecedent items. The strength of an association rule is measured by two metrics: support and confidence. Support is the percentage of transactions that contain both the antecedent and consequent items, while confidence is the percentage of transactions that contain the consequent item given the antecedent items.

Evaluation of association rules is the next step in the association analysis process. Various metrics can be used to evaluate the quality and usefulness of the generated rules. These metrics include lift, conviction, and leverage. Lift measures the deviation from independence of the antecedent and consequent items, while conviction quantifies the predictive power of the rule. Leverage measures the difference between the observed frequency of the rule and the frequency expected under independence.

Finally, the last step in association analysis is interpretation and deployment. Once the association rules are generated and evaluated, they need to be interpreted in a meaningful way. This requires domain knowledge and expertise to understand the implications of the rules and make informed decisions based on them. The deployment of association rules involves integrating them into business processes, such as marketing campaigns, recommendation systems, or supply chain management.

In conclusion, association analysis is a valuable data mining technique that helps to uncover relationships and patterns in large datasets. By following the steps of data preparation, identifying frequent itemsets, generating association rules, evaluating the rules, and interpreting and deploying them, organizations can gain insights and make data-driven decisions. The significance of association analysis lies in its ability to improve decision-making processes and optimize business operations.