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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
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Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
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Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
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Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
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What are Association Rules in Data Mining?

Sakshi Raheja
Sakshi Raheja Last Updated : 16 Feb, 2024
5 min read

Introduction

The evolution of humans from coal mining to data mining holds immense contributions to human growth and technological development. Changing the extent of physical work involved, the weight has now shifted towards mental exertion to perform this new type of mining. The data mining process includes multiple aspects, including the Association rule, which is significant due to its practical contribution to understanding the customers and driving business growth. Do you have the exact requirements? Are you interested in enhancing your knowledge to bring an exponential rise in customer satisfaction? Are you aiming to develop a better recommendation system competitive enough with big brand names? Here is a brief introduction to key concepts and fundamentals of association rules in data mining.

Association rules in data mining

Learning Objectives

  • Comprehend the essence of association rules as if/then statements revealing relationships within data.
  • Identify and differentiate applications such as market basket analysis, fraud detection, and recommendation systems, showcasing association rules’ versatility and practical significance.
  • Gain insight into how association rules work, exploring the role of cardinality, support, confidence, and lift in predicting and evaluating relationships within datasets.

Table of contents

What Are Association Rules in Data Mining?

Defined by their names, association rules are if/then statements that identify the relationships or dependencies between the data. With the characteristic property of suiting numeric and non-numeric categorical data, it is often applied in market basket analysis and other applications. It can uptake data from relational and transactional databases and other data sources.

The association rule has two parts: antecedent or if and consequent or then. The antecedent is the first part available in data, while the resulting is the resultant part available in combination with the antecedent. For instance, the example of market basket analysis will be: “If a customer buys running shoes, then there is a likelihood that they will also buy Energy bars.” here, running shoes are antecedent, and energy bars are consequent. The example more particularly targets the fitness enthusiast audience.

What Are Use Cases for Association Rules in data Mining?

There is a wide variety of applications for association rules. The top three association rules in data mining examples are:

Market Basket Analysis: An example of a shopping combination can be a purchase of yogurt, and granola is likely to be associated with purchasing berries. It indicates the significance of the association rule in analyzing purchasing habits and requirements. The practical usage of interpretation is seen in developing suitable combination offers, optimizing product placements, and increasing sales.

Fraud Detection: Here, the combination of use is identifying a purchase pattern, its location, and frequency. Recognition of the same helps flag fraudulent activities and take preventive measures from the same IP address.

Recommendation systems: These include detecting the usage patterns from browsing history and previous purchases to predict the future requirements of the user. The recommendations are based on the same. Expanding the usage from marketing is significant in music and shows-based services as well.

Alt-text: Applications of Association Rule

Source: Dataaspirant

How Do Association Rules Work?

The prediction in the association rule explained previously with examples, is calculated based on cardinality, support, and confidence. Cardinality refers to the relation between two items, which proportionally increases with the number of objects. The support indicates the frequency of the statements, and then the confidence informs the frequency of truthfulness of these relationships. Explain the association rules work by determining the rules governing the reason and situation where the combination may occur. For instance, the preferred healthy and less time-consuming breakfast option combines yogurt with granola and berries.

Often, in practical situations, the numbers get unrealistic. Some statistically independent items with the least purchase combination might come together with a significantly high percentage in practical usage. For instance, statistically, lesser chances of combined purchase of beer and diapers occur while real-world statistics are comparatively higher. The increase in statistics is a lift.

Measures of the Effectiveness of Association Rules

The effectiveness of association rules is primarily measured by support, confidence, and lift. The support refers to the frequency, and the high support indicates the commonness of quantity in the dataset. The confidence measures the reliability of the association rule. The high confidence suggests A and B are proportional and hence increase in direct relation to each other.

Lift compares the dependency of the item. If the statistical and practical numbers are the same or the antecedent and consequent are the same, the lift will be 1, and the associated objects are independent. The objects depend on each other if lift > 1 and the antecedent is greater than the consequent. Moreover, the combination negatively impacts each other if the consequent is more than the antecedent with lift < 1.

Rule Evaluation Metrics

Source: Data Mining Map

Association Rule Algorithms

Three algorithms generate association rules. These are stated as follows:

Apriori Algorithm

The association rules in the apriori algorithm are generated through frequent transaction datasets. Often used for market basket analysis, it uses techniques like Breadth-first search and Hash tree. Providing the information on combined products bought together, it also serves medical purposes by finding drug reactions for patients.

Eclat Algorithm

Also known as Equivalent Class Transformation, it uses a depth-first search technique. Providing quick and accurate execution, it also deals with transaction databases. The ELCAT algorithm uses less storage and works without repeated scanning of data for computing the individual support values. Instead, it uses transaction ID Sets or Tidsets for computation purposes.

F-P Growth Algorithm

Referred to as Frequent pattern growth, it is a further enhanced version of the Apriori algorithm. It is analyzed through two steps. The first is database conversion into a tree structure, thus earning the name due to the depiction of frequent patterns. The second step is the representation format, which further eases extracting the most frequent patterns.

Association Rule Mining Technique

Source: ResearchGate

Conclusion

Data mining refers to extracting information from comprehensive sourced datasets. Association rule mining is the method for identifying the correlations, patterns, associations, or causal structures in the datasets. With the immense scope of applicability in retail, healthcare, fraud detection, biological research, and multiple other fields, the association rule works through the if/then statement. Support, confidence, and lift play critical roles in evaluating its effectiveness. Moreover, the development of the association rules occurs through three algorithms. Please introduce yourself to more important concepts along with association rules in data mining in detail with our data science course.

Key Takeaways

  • Association rules find practical use in diverse fields, such as optimizing product placements in market basket analysis, flagging fraudulent activities in fraud detection, and enhancing user experience through recommendation systems.
  • Support, confidence, and lift are crucial metrics for evaluating the effectiveness of association rules, providing insights into the frequency, reliability, and dependency of identified relationships.
  • Explore three key algorithms—Apriori, Eclat, and F-P Growth—that drive the generation of association rules, each offering unique advantages in terms of execution speed, data scanning efficiency, and scope of application.

Frequently Asked Questions

Q1. What are the disadvantages of association rule mining?

A. The drawbacks are many rules, lengthy procedures, low performance, and the inclusion of many parameters in association rule mining.

Q2. Are there types of association rules?

A. Yes, there are four types of association rules in mining. These are multi-relational, quantitative, generalized, and interval information association rules.

Q3. Enlist some tools important for association rule mining.

A. The tools of significance in the association rule are RapidMiner, WEKA, and Orange.

Sakshi Raheja
Sakshi Raheja

I am a passionate writer and avid reader who finds joy in weaving stories through the lens of data analytics and visualization. With a knack for blending creativity with numbers, I transform complex datasets into compelling narratives. Whether it's writing insightful blogs or crafting visual stories from data, I navigate both worlds with ease and enthusiasm. 

A lover of both chai and coffee, I believe the right brew sparks creativity and sharpens focus—fueling my journey in the ever-evolving field of analytics. For me, every dataset holds a story, and I am always on a quest to uncover it.

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