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What Are The Association Rules In Data Mining?


Data mining is the process of searching through big data sets to find patterns and relationships that, when analyzed, can assist in resolving issues that arise in commercial enterprises. Enterprises now have the ability to forecast future trends and make better-informed business decisions thanks to the methodologies and tools for data mining.

Association rules in data mining act as detective agents in large datasets, revealing connections and relationships between items or events that often occur together. These statements follow the "if-then" format and may be used to indicate the possibility of links between data items. These rules can be applied to massive data sets that are stored in a variety of databases. Association rule mining is frequently utilized for various purposes, including, but not limited to, the detection of sales correlations in transactional data or medical data sets. For an in-depth understanding of association rules, our Data scientist course online helps you explore more about association rules data mining, the most effective tool of data science. 

What is Association Rule in Data Mining?

Mining for valuable connections and patterns buried deep within enormous data sets is the objective of the technique known as association rule mining. This rule provides an illustration of the frequency with which a particular item set appears in a trade. Let’s expand our focus to include mining multilevel, multidimensional association rules, and quantitative association rules in transactional and/or relational databases and data warehouses in order to accommodate the needs of a wider range of applications. Multilevel association rules involve concepts at several levels of abstraction are involved in multilevel association rules. Rules that relate a customer's age to their purchasing habits are an example of multidimensional association rules. For quantitative association rules, numerical criteria with an underlying value hierarchy are required (e.g., age).

Types of Association Rules in Data Mining

  1. Multilevel - Finding reliable correlations between data points can be difficult in many situations because there is insufficient information available at lower or more fundamental levels of abstraction. The strong correlations observed at the highest levels of abstraction likely represent the common sense we all share.Even information that is patently clear to one person could come across as entirely novel to another.As a result, data mining systems need to allow users to mine association rules across several levels of abstraction, along with sufficient mobility to travel between different areas in a relaxed manner.
  2.  Multi-Dimensional or Inter-Dimensional - When an association rule has two or more dimensions or predicates, we call it a multidimensional association rule. The following are some examples:(X, "20-29") (X, "Student") =>(X) buys (X,"Laptop")Inter-dimensional association rules, like the one presented here, have at least three predicates (here, age, occupation, and purchases) that each appears exactly once. Hybrid-dimension association rules are rules that contain numerous repetitions of some predicates or rules that have repeated predicates.Specifically, consider-If (X) is a person's age and (X) is a product they've purchased in the past (X, "Laptop"), then (X) is a product they're likely to (X, "Printer") The characteristics of the database should be either quantitative or categorized.Nominal characteristics, which are synonymous with categorical attributes, are those that may take on a limited number of discrete values.
  3. Quantitative Association Rule - When compared to other types of association rules, this one is quite exceptional. The distinguishing feature of quantitative association rules is the inclusion of numeric characteristics in at least one attribute. In contrast, the generalized association rule's left and right sides are comprised of category characteristics.

Techniques of Association Rules in Data Mining

Association rule mining finds its way into numerous applications, with some widely recognized ones being:

Market Basket Analysis

Frequent itemset mining when applied to large transactional or relational datasets, has the potential to find relationships and patterns between data points that were previously unknown. As companies collect and store ever-greater amounts of data, there is a rising interest in mining such patterns from databases. This interest has grown in recent years. The discovery of intriguing connection correlations across massive volumes of business transaction information may prove beneficial for a wide range of business decisions, including compiling catalogs, implementing cross-marketing strategies, and investigating customer behavior.The frequent item mining technique known as market basket analysis is quite widespread. This technique disassembles the "baskets" of consumers in order to analyze the connections between the many different items that those customers buy. Retailers may profit from the identification of such links by knowing which goods are typically purchased together by consumers. Retailers may gain from the discovery of such relationships. Suppose, for example, that customers are consistently making purchases.


Data analysis and the identification of recurrent if/then relationships are the foundations upon which association rules are built. Then, the following two factors determine how the significant linkages manifest themselves:

The Support value represents how often the if/then connection is found in the database.The degree of confidence indicates how often these correlations have been observed to hold true. Join a self-learning data science training course to understand association rules in data mining better. 

Association Rule Mining Example: Market Based Analysis

Assume for a moment that you are the manager of an AllElectronics store and are interested in customers' purchasing habits. You can get the answer to your question by employing a strategy known as "market basket analysis" to research individual customers' purchase histories. After the data has been collected, it may be used to inform future marketing and advertising activities, in addition to the construction of catalogs. For instance, the layout of shop premises may be influenced by market basket research if it is done properly. One strategy is to assemble frequently purchased items together and position them so that they are close to one another. This increases the probability that the items will be purchased together. If customers who buy PCs also buy antivirus software, placing the hardware display next to the software display could enhance sales of both items. This is because customers who buy PCs are also likely to buy antivirus software. One possibility is to divide the store into two independent parts, one for the hardware and one for the software, with the intention of encouraging customers to make further purchases as they move between the two.

For instance, a customer who has recently acquired an expensive computer could peruse the security systems area on their way to the software department, where they expect to purchase antivirus software and end up purchasing both of these items due to their browsing. Research on market baskets may also provide information to retailers on which items should have lower prices, in addition to directing pricing decisions. Customers with a history of purchasing printers and personal computers simultaneously are more likely to be influenced to acquire both during a sale of the former product.Consider the variety of products available for purchase to be the universe. A matching Boolean variable for each item indicates whether or not it is currently in stock. After that, you can use a boolean vector of values for these variables to create a representation of each basket.By doing an analysis of the boolean vectors, it is possible to determine which products are frequently purchased together. Association rules are a handy tool for expressing the patterns shown here. One such piece of data shows the observation that people who buy computers also commonly buy antivirus software. This was shown to be the case."Computer" and "Antivirus Software" are examples of an association rule with 2% support and 60% confidence.

One method for determining whether or not a regulation is popular is to examine the extent to which it enjoys general support and widespread confidence. They are a gauge for determining the applicability and dependability of recently discovered rules. If 2% of all transactions support the association rule, then it is reasonable to assume that 2% of all computer systems also buy antivirus software. This may be deduced from the fact that 2% of all transactions support the association rule. There is a level of assurance equal to sixty percent that six out of ten customers who purchased a computer also purchased the supplementary software. In most cases, an association rule is only interesting if it satisfies the minimum requirements for both the degree of support it receives and its confidence level. Users or subject matter experts (also known as SMEs) are able to determine these cutoffs. More research may result in the discovery of intriguing statistical connections between various subjects that are connected. 

Mathematically, the lift is defined as the fraction of the product of the probabilities of x and y devoted to the combined probability of x and y.

Lift = P(x,y) / [P(x)P(y)]

The joint probability of two things is equal to the product of their probabilities if they can be treated as independent by statistics. Alternatively stated: P(x,y)=P(x)P(y), where Lift factor = 1. In this context, it's worth noting that anti-correlation can provide Lift values below 1, which would represent objects that hardly ever coexist.

Data scientists would be hard-pressed to name all the patterns they've discovered thanks to Association Rule Mining. For better understanding, our Data science tutorial will help you to explore the world of data science and prepare to face the challenges.

Association Rule Mining Python

Association rule mining is a technique used in data mining and machine learning to discover patterns or relationships between items in large datasets. It involves identifying frequent item sets, which are combinations of items that appear together frequently, and generating association rules based on these item sets.Python is a popular data science and machine learning programming language due to its simplicity, flexibility, and powerful libraries. Several libraries are available for implementing association rule mining in Python, such as mlxtend and Orange3-Associate. These libraries provide various algorithms for discovering frequent item sets, such as the Apriori algorithm, FP-growth algorithm, etc.The Apriori algorithm is one of Python's most commonly used algorithms for association rule mining. This algorithm works by first identifying all individual items that frequently occur in the dataset (called 1-itemsets), then iteratively finding larger sets of items (2-itemsets, 3-itemsets, etc.) that occur frequently based on the minimum support threshold set by the user.

To implement the Apriori algorithm using mlxtend library in Python, we need to follow the below steps:

1) Importing Required Libraries
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
import pandas as pd
2) Loading Dataset into Pandas Data Frame
3) Converting Categorical Variables into Binary Values:

df = pd.get_dummies(df)

4) Generating Frequent Itemset Using The Apriori Function

Here min_support refers to the minimum support threshold value i.e., the frequency of occurrence required for an itemset to be considered frequent. At the same time, max_len determines the maximum length of generated frequent itemset.

5) Generating Association Rules from Frequent Itemset

Here metric refers to criteria for evaluating association rules while min_threshold is used as a filter criterion.

Implementing association rule mining using Python and libraries like mlxtend can help us discover valuable insights from large datasets. The library's Apriori algorithm helps identify frequent item sets and generate association rules based on these itemsets. Following the above-mentioned steps, we can easily implement association rule mining using Python and extract useful information from our data.

Applications of Association Rule Mining

Association rule mining can be used for various purposes. One of the most significant uses of association rule mining is to do market-based research. Here is the list of other applications where association rule mining can be successfully used: 

  1. When renting a car or staying in a hotel and paying for it with a credit card, you can learn more about the next product the customer will likely buy.
  2. Call waiting, forwarding, DSL, speed call, and other optional services that teleconnection users can buy help decide how to bundle these functions to make the most money.
  3. People who use retail banking services (like money market accounts, certificates of deposit, investment services, car loans, etc.) will likely need other services.
  4. When a group of insurance claims is unusual, it can be a sign of fraud and lead to a deeper investigation.
  5. Doctors can predict possible side effects using a patient's medical history and a set of treatments. 

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The association rule is a helpful way to look at data sets. Bar-code scanners in supermarkets are used to collect the information. Such databases have a lot of transaction records that list everything a customer bought in one purchase. So that the manager can see if certain groups of items are always bought together and use this information to change store layout, cross-selling, and promotions. Understanding association rules in data science begins with understanding data science; you can get an insight into the same through our various professional certification courses.   

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