This article provides a brief explanation of the FP Growth technique of Frequent Pattern Mining.
What is the FP Growth Algorithm?
Frequent pattern mining (previously known as Association) is an analytical algorithm that is used by businesses and, is accessible in some self-serve business intelligence solutions. The FP Growth analytical technique finds frequent patterns, associations, or causal structures from data sets in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories.
When considering a particular set of transactions, association rule mining aims to find the rules that will enable businesses to predict the occurrence of a specific item based on the occurrences of the other items in the transaction.
This technique can used to analyze numerous types of datasets.
- Basket data analysis – To analyze the association of purchased items in a single basket or single purchase.
- Cross marketing/Selling – To work with other businesses that complement your business, but not your competitors. For example, vehicle dealerships and manufacturers have cross marketing campaigns with oil and gas companies for obvious reasons.
- Catalog Design – The selection of items in a business catalog are often designed to complement each other so that buying one item will lead to buying of another. So these items are often complements or related.
- Medical Treatments – Each patient is represented as a transaction containing the ordered set of diseases, and which diseases are likely to occur simultaneously or sequentially and can therefore be predicted.
How Does a Business Use the FP Growth method of Frequent Pattern Mining to Analyze Data?
Let’s look at a few use cases, where the FP Growth method of Frequent Pattern Mining can be used to benefit the organization.
Use Case – 1
Business Problem: A retail store manager wants to conduct Market Basket analysis to come up with better strategy of products placement and product bundling.
Business Benefit: The darker segments reveal the ideal methods of product bundling and placement to increase cross-sales. Based on the association rules generated, the store manager can strategically place the products together or in sequence leading to growth in sales and in turn revenue of the store. The business can develop promotions and offers, e.g., “Buy this and get this free” or “Buy this and get % off on another product”.
Use Case – 2
Business Problem: A bank marketing manager wishes to analyze which products are frequently and sequentially bought together. Each customer is represented as a transaction containing the ordered set of products, and which products customers are likely to purchase simultaneously, and sequentially.
Business Benefit: Based on the rules generated, the organization can determine which banking products can be cross sold to each existing or prospective customer to drive sales and bank revenue. For instance, if saving, personal loan and credit card are frequently sequentially bought, then a new saving account customer can be cross sold with personal loan and credit card services and products.
Frequent Pattern Mining and the FP Growth analytical technique are useful to identify patterns of purchases, behaviors and frequent and sequential occurrences based on historical data and demographics.
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