analysis uses purchase transaction data to identify associations between products or combinations of products and services that occur together frequently.

The analysis you're referring to is called "Market Basket Analysis". It's a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. This technique is commonly used in the retail industry. Here are the steps:

1. Data Collection: The first step is to collect data. This data is typically collected at the point of sale where the barcode is scanned for billing. All the transactions throughout the day are stored in a database.

2. Data Cleaning: The collected data is cleaned by removing any unwanted noise or errors.

3. Data Analysis: The cleaned data is then analyzed. The goal is to find interesting relationships or associations among a set of items. This is done using algorithms like Apriori, FP-Growth, etc.

4. Rule Generation: Based on the analysis, certain rules are generated. These rules are the associations or patterns that the analysis has found. For example, if a person buys bread and butter, they are likely to buy milk.

5. Rule Evaluation: The generated rules are then evaluated. This is done to determine the effectiveness of the rules and to decide whether they can be used to increase sales.

6. Implementing Strategies: Based on the evaluated rules, marketing strategies are implemented. This could be in the form of product placements, sales promotions, etc.

7. Review: The final step is to review the effectiveness of the strategies implemented. This is done by comparing sales before and after the implementation of the strategies.