Association Rule Mining: Importance and Steps

As the name implies, association rules are simple If/Then statements that aid in the discovery of relationships between seemingly independent relational databases or other data repositories.

 

Because most machine learning algorithms work with numerical datasets, they are mathematical in nature. Association rule mining, on the other hand, is appropriate for non-numeric, categorical data and requires a little more than simple counting.

 

Association rule mining is a procedure that seeks out frequently occurring patterns, correlations, or associations in datasets from various types of databases, including relational databases, transactional databases, and other types of repositories.

 

Also Read | Introduction to Database Management System

 

What is Association Rule Mining?

 

Association rule mining is a procedure used to discover frequent patterns, correlations, associations, or causal structures in data sets stored in various types of databases such as relational databases, transactional databases, and other types of data repositories.

 

The goal of association rule mining, given a set of transactions, is to find the rules that allow us to predict the occurrence of a specific item based on the occurrences of the other items in the transaction. The data mining process of discovering the rules that govern associations and causal objects between sets of items is known as association rule mining.

 

So, in a given transaction involving multiple items, it attempts to identify the rules that govern how or why such items are frequently purchased together. For example, peanut butter and jelly are frequently purchased together because many people enjoy making PB&J sandwiches.

 

Association Rule Mining is a Data Mining technique for discovering patterns in data. Association Rule Mining patterns represent relationships between items. When combined with sales data, this is known as Market Basket Analysis.

 

Fast-food restaurants, for example, discovered early on that people who eat fast food tend to be thirsty due to the high salt content and end up buying Coke. They took advantage of this by creating combo meals that combine food that is sure to make you thirsty with Coke as part of the meal.

 

Maybe these chains didn't use data mining to make this business decision, but maybe they did. In any case, it has contributed to their increased profits. The purpose of this example is to demonstrate that Association Rules represent relationships, which must be interpreted before they can be used in strategies.

 

Association rules are used in data science to discover correlations and co-occurrences between data sets. They are best suited for explaining patterns in data from seemingly unrelated information repositories, such as relational and transactional databases. The use of association rules is sometimes referred to as "association rule usage."

 

Importance of Association Rule Mining

 

Here are some of the reasons why Association Rule Mining is important and such an effective business tool.

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Importance of Association Rule Mining

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1. Aids businesses in developing sales strategies

 

The ultimate goal of any business is to become profitable. This entails attracting more customers and increasing sales. They can develop better strategies by identifying products that sell better together. For example, knowing that people who buy fries almost always buy Coke can be used to boost sales.

 

2. Assists businesses in developing marketing strategies

 

Attracting customers is a critical component of any business. Understanding which products sell well together and which do not is essential when developing marketing strategies.

 

This includes sales and advertisement planning, as well as targeted marketing. For example, knowing that some ornaments do not sell as well as others during the holiday season may enable the manager to offer a discount on the infrequent ornaments.

 

3. It aids in shelf-life planning

 

Knowledge of association rules can help store managers plan their inventory and avoid losing money by overstocking low-selling perishables.

 

For example, if olives aren't selling well, the manager won't stock up on them. However, he wishes to ensure that the existing stock is sold before the expiration date. Given that people who buy pizza dough also buy olives, the olives can be sold at a lower price when purchased with the pizza dough.

 

4. It aids in-store organization

 

Products that have been shown to increase the sales of other products can be moved closer together in the store. For example, if butter sales are driven by bread sales, they can be moved to the same aisle in the store.

 

Association Rule Mining is also used in media recommendations (movies, music, etc.), webpage analysis (people who visit website A are more likely to visit website B), and so on.

 

Also Read | Applications of Data Mining in Retail

 

Steps in Association Rule Mining

 

Association Rules are based on if/then statements. These statements aid in the discovery of associations between independent data in a database, relational database, or other data repository. These rules are used to determine the relationships between objects that are commonly used together.

 

Support and confidence are the two primary patterns used by association rules. The method searches for similarities and rules formed by decomposing data for commonly used if/then patterns. Association rules are typically used to simultaneously satisfy user-specified minimum support and a user-specified minimum resolution. To implement association rule learning, various algorithms are used.

 

Association Rule Mining can be described as a two-step process.

 

Step 1: Locate all frequently occurring itemsets

 

An itemset is a collection of items found in a shopping basket. It can include many products. For example, [bread, butter, eggs] is a supermarket database itemset.

 

A frequently occurring item set is one that frequently appears in a database. This raises the issue of how frequency is defined. This is where your support comes into play. The frequency of an item in the dataset is used to calculate its support count.

 

The number of supporters can only speak to the frequency of an item set. It does not consider the relative frequency, or the frequency in relation to the number of transactions. This is referred to as an itemset's support. The frequency of an item set in relation to the number of transactions is referred to as its support.

 

Step 2: Create strong association rules using the frequently used itemsets

 

Association rules are created by constructing associations from the frequent itemsets created in step 1. To find strong associations, this employs a metric known as confidence.

 

The Apriori algorithm is one of the most fundamental Association Rule Mining algorithms. It is based on the idea that "having prior knowledge of frequent itemsets can generate strong association rules." The term Apriori refers to prior knowledge.

 

Apriori discovers frequent itemsets through a process known as candidate itemset generation. This is an iterative approach that uses k-itemsets to explore (k+1)-itemsets. The set of frequent 1-itemsets is found first, followed by the set of frequent 2-itemsets, and so on until no more frequent k-itemsets can be found.

 

An important property known as the Apriori property is used to reduce the search space to improve the efficiency of the level-wise generation of frequent itemsets. According to the Apriori Property, "all non-empty subsets of a frequent itemset must also be frequent."

 

This means that if an item is frequent, its subsets will also be frequent. For example, if [Bread, Butter] is a frequent item set, [Bread] and [Butter] must be frequent individually as well.

 

Also Read | Top Data Mining Tools
 

 

Apriori Algorithm for Mining Association

 

Apriori is one of several statistical algorithms that have been developed to implement association rule mining. In this article, we will look at the theory behind the Apriori algorithm and then implement it in Python.

 

The Apriori algorithm is made up of three major components:

 

• Support

 

• Confidence

 

• Lift

 

With the help of an example, we will explain these three concepts.

 

Assume we have a database of 1,000 customer transactions and want to find the Support, Confidence, and Lift for two items, such as burgers and ketchup. One hundred transactions contain ketchup, while 150 contain a burger. In 50 of the 150 transactions where a burger is purchased, ketchup is also included. 

 

1. Support

 

Support refers to an item's default popularity and can be calculated by dividing the number of transactions containing a specific item by the total number of transactions. Assume we want to find help for item B. This can be calculated as follows:

 

Support(B) = (Transactions with (B))/ (Total Transactions)

 

2. Confidence

 

If item A is purchased, confidence refers to the likelihood that item B will be purchased as well. It can be calculated by dividing the number of transactions in which A and B are purchased together by the total number of transactions in which A is purchased. It can be expressed mathematically as:
 

Confidence(AB) = (Transactions with both (A and B))/ (Transactions containing A)

 

3. Lift 

 

Lift(A -> B) denotes the increase in the sale ratio of B when A is sold. Lift(A -> B) is computed by dividing Confidence(A -> B) by Support (B). It can be expressed mathematically as:

 

Lift(AB) = (Ab) Confidence/(B) Support

 

Also Read | Top Data Mining Algorithms

 

 

Conclusion

 

The strength or dependability of association rule mining is critical to consider. As association rule mining uncovers interesting associations and relationships among large sets of data items, it reveals how frequently a particular item set appears in a transaction. Association rules are useful in data mining for analyzing and forecasting customer behavior.

 

However, the main disadvantage of association rule algorithms is that they produce boring rules, have many discovered rules, and perform poorly. The employed algorithms have too many parameters for someone who is not a data mining expert, and the produced rules are too many, with most of them being uninteresting and having low comprehensibility.

 

With the rapid growth of e-commerce websites and the general trend in industries (particularly retail) to turn to data for answers, every organization is looking for more opportunities for best product bundles to run discounts and promotions on.

 

In exchange for these decisions, it is expected that sales will increase and inventory levels will decrease. Analyzing "what is bought together" can often produce very interesting results.

 

A rule-based method for discovering relationships between variables in large datasets is association rule learning. The retail products will be our variables in the case of retail POS (point-of-sale) transactions analytics. It basically finds strong associations (rules) with some "strength" level represented by several parameters.