Frequent Pattern Mining: An Easy Guide (2021)

Introduction

The issue of frequent pattern mining has been studied in the literature because of its numerous applications to a range of data mining complications such as clustering and classification. On top of that, frequent pattern mining is also extremely applied in varied domains like spatiotemporal data, biological data and software bug detection. The algorithmic facet of frequent pattern mining has been researched extensively.

1. What is Frequent Pattern Mining?
2. How does Frequent Pattern Mining Support Business Analysis?
3. To understand the value of this applied technique.

1. What is Frequent Pattern Mining?

Frequent Pattern Mining is also known as the Association Rule Mining. Finding frequent patterns, causal structures and associations in data sets and is an inquisitive process called pattern mining. When a series of transactions are given, pattern mining’s main motive is to find the rules that enable us to speculate a certain item based on the happening of other items in the transaction.

For instance, a set of items, such as pen and ink, often appears together in a set of data transactions, is called a recurrent item set. Purchasing a personal computer, later a digital camera, and then a hard disk, if all these events repeatedly occur in the history of shopping database, it is a (frequent) sequential pattern. If the occurrence of a substructure is regular in a graph database, it is called a (frequent) structural pattern.

2. How does Frequent Pattern Mining Support Business Analysis?

Pattern mining is applicable in assessing data for varied business operations and industries.

• Basket Data Analysis: It helps in scrutinising the association between the items purchased in a single purchase.
• Selling and Cross Marketing: To identify and operate with businesses that complement our own business and disregard competitors. For instance, manufacturers and vehicle dealerships get into cross-marketing campaigns with gas and oil companies for apparent reasons.
• Catalog Design: Catalogs are designed in such a way that the items of selection act as mutual complements, which results in buying of one item will eventually lead to purchasing another, therefore act as complements or are closely related.
• Medical Treatments: The listed and diagnosed set of illnesses of every patient is depicted as a transaction, from which the diseases that are probable to occur sequentially/ simultaneously can be anticipated.

3. To understand the value of this applied technique.

Let’s look at two business scenarios: 

• Case One

Problem: A retail manager of a store to prepare a better strategy of product bundling and product placement wants to administer a Market Basket Analysis.

Business Solution: Elicited from the rules of pattern mining, the income of the store can be increased by strategically placing the complementary products together or in series, which leads to an upswing in sales. Offers like “Buy this and enjoy % off ”, “Buy this and get this free” or “Buy one and get three” can be developed based on the rules designed.

• Case Two

Problem: The fulfil the purpose of analyzing the products which are sequentially and frequently purchased together beneficial to a bank-marketing manager.

Business solution: Based off of the rules of pattern mining, cross-selling of bank products to every prospective or existing customer to increase and manifold bank revenue and sales. For example, if personal loan, savings and credit cards are sequentially bought, then along with a credit card and personal loan, a new saving account can be cross-sold to a customer.

Abundant literature has been dedicated to this research, and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent mining in transaction databases to numerous research frontiers and their broad applications.

Conclusion

Finding frequent patterns is crucial in correlations, mining associations, and numerous other interesting relationships present in data. It is worthwhile in data classification, indexing and clustering. Frequent pattern mining is a principal data mining task and an important theme in data mining research. Ample literature and research have been written and conducted, leading to stupendous progress being made, within an array of scalable and efficient algorithms for frequent item-set mining in transaction databases to various research frontiers and their broad applications. With numerous applications, especially in the field of business, frequent pattern mining is immensely beneficial. 

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