- ML - Home
- ML - Introduction
- ML - Getting Started
- ML - Basic Concepts
- ML - Ecosystem
- ML - Python Libraries
- ML - Applications
- ML - Life Cycle
- ML - Required Skills
- ML - Implementation
- ML - Challenges & Common Issues
- ML - Limitations
- ML - Reallife Examples
- ML - Data Structure
- ML - Mathematics
- ML - Artificial Intelligence
- ML - Neural Networks
- ML - Deep Learning
- ML - Getting Datasets
- ML - Categorical Data
- ML - Data Loading
- ML - Data Understanding
- ML - Data Preparation
- ML - Models
- ML - Supervised Learning
- ML - Unsupervised Learning
- ML - Semi-supervised Learning
- ML - Reinforcement Learning
- ML - Supervised vs. Unsupervised
- Machine Learning Data Visualization
- ML - Data Visualization
- ML - Histograms
- ML - Density Plots
- ML - Box and Whisker Plots
- ML - Correlation Matrix Plots
- ML - Scatter Matrix Plots
- Statistics for Machine Learning
- ML - Statistics
- ML - Mean, Median, Mode
- ML - Standard Deviation
- ML - Percentiles
- ML - Data Distribution
- ML - Skewness and Kurtosis
- ML - Bias and Variance
- ML - Hypothesis
- Regression Analysis In ML
- ML - Regression Analysis
- ML - Linear Regression
- ML - Simple Linear Regression
- ML - Multiple Linear Regression
- ML - Polynomial Regression
- Classification Algorithms In ML
- ML - Classification Algorithms
- ML - Logistic Regression
- ML - K-Nearest Neighbors (KNN)
- ML - Naïve Bayes Algorithm
- ML - Decision Tree Algorithm
- ML - Support Vector Machine
- ML - Random Forest
- ML - Confusion Matrix
- ML - Stochastic Gradient Descent
- Clustering Algorithms In ML
- ML - Clustering Algorithms
- ML - Centroid-Based Clustering
- ML - K-Means Clustering
- ML - K-Medoids Clustering
- ML - Mean-Shift Clustering
- ML - Hierarchical Clustering
- ML - Density-Based Clustering
- ML - DBSCAN Clustering
- ML - OPTICS Clustering
- ML - HDBSCAN Clustering
- ML - BIRCH Clustering
- ML - Affinity Propagation
- ML - Distribution-Based Clustering
- ML - Agglomerative Clustering
- Dimensionality Reduction In ML
- ML - Dimensionality Reduction
- ML - Feature Selection
- ML - Feature Extraction
- ML - Backward Elimination
- ML - Forward Feature Construction
- ML - High Correlation Filter
- ML - Low Variance Filter
- ML - Missing Values Ratio
- ML - Principal Component Analysis
- Reinforcement Learning
- ML - Reinforcement Learning Algorithms
- ML - Exploitation & Exploration
- ML - Q-Learning
- ML - REINFORCE Algorithm
- ML - SARSA Reinforcement Learning
- ML - Actor-critic Method
- ML - Monte Carlo Methods
- ML - Temporal Difference
- Deep Reinforcement Learning
- ML - Deep Reinforcement Learning
- ML - Deep Reinforcement Learning Algorithms
- ML - Deep Q-Networks
- ML - Deep Deterministic Policy Gradient
- ML - Trust Region Methods
- Quantum Machine Learning
- ML - Quantum Machine Learning
- ML - Quantum Machine Learning with Python
- Machine Learning Miscellaneous
- ML - Performance Metrics
- ML - Automatic Workflows
- ML - Boost Model Performance
- ML - Gradient Boosting
- ML - Bootstrap Aggregation (Bagging)
- ML - Cross Validation
- ML - AUC-ROC Curve
- ML - Grid Search
- ML - Data Scaling
- ML - Train and Test
- ML - Association Rules
- ML - Apriori Algorithm
- ML - Gaussian Discriminant Analysis
- ML - Cost Function
- ML - Bayes Theorem
- ML - Precision and Recall
- ML - Adversarial
- ML - Stacking
- ML - Epoch
- ML - Perceptron
- ML - Regularization
- ML - Overfitting
- ML - P-value
- ML - Entropy
- ML - MLOps
- ML - Data Leakage
- ML - Monetizing Machine Learning
- ML - Types of Data
- Machine Learning - Resources
- ML - Quick Guide
- ML - Cheatsheet
- ML - Interview Questions
- ML - Useful Resources
- ML - Discussion
Machine Learning - Apriori Algorithm
Apriori is a popular algorithm used for association rule mining in machine learning. It is used to find frequent itemsets in a transaction database and generate association rules based on those itemsets. The algorithm was first introduced by Rakesh Agrawal and Ramakrishnan Srikant in 1994.
The Apriori algorithm works by iteratively scanning the database to find frequent itemsets of increasing size. It uses a "bottom-up" approach, starting with individual items and gradually adding more items to the candidate itemsets until no more frequent itemsets can be found. The algorithm also employs a pruning technique to reduce the number of candidate itemsets that need to be checked.
Here's a brief overview of the steps involved in the Apriori algorithm −
Scan the database to find the support count of each item.
Generate a set of frequent 1-itemsets based on the minimum support threshold.
Generate a set of candidate 2-itemsets by combining frequent 1-itemsets.
Scan the database again to find the support count of each candidate 2-itemset.
Generate a set of frequent 2-itemsets based on the minimum support threshold and prune any candidate 2-itemsets that are not frequent.
Repeat steps 3-5 to generate candidate k-itemsets and frequent k-itemsets until no more frequent itemsets can be found.
Example
In Python, the mlxtend library provides an implementation of the Apriori algorithm. Below is an example of how to use use the mlxtend library in conjunction with the sklearn datasets to implement the Apriori algorithm on iris dataset.
from mlxtend.frequent_patterns import apriori
from mlxtend.preprocessing import TransactionEncoder
from sklearn import datasets
# Load the iris dataset
iris = datasets.load_iris()
# Convert the dataset into a list of transactions
transactions = []
for i in range(len(iris.data)):
transaction = []
transaction.append('sepal_length=' + str(iris.data[i][0]))
transaction.append('sepal_width=' + str(iris.data[i][1]))
transaction.append('petal_length=' + str(iris.data[i][2]))
transaction.append('petal_width=' + str(iris.data[i][3]))
transaction.append('target=' + str(iris.target[i]))
transactions.append(transaction)
# Encode the transactions using one-hot encoding
te = TransactionEncoder()
te_ary = te.fit(transactions).transform(transactions)
df = pd.DataFrame(te_ary, columns=te.columns_)
# Find frequent itemsets with a minimum support of 0.3
frequent_itemsets = apriori(df, min_support=0.3, use_colnames=True)
# Print the frequent itemsets
print(frequent_itemsets)
In this example, we load the iris dataset from sklearn, which contains information about iris flowers. We convert the dataset into a list of transactions, where each transaction represents a single flower and contains the values for its four attributes (sepal_length, sepal_width, petal_length, and petal_width) as well as its target label (target). We then encode the transactions using one-hot encoding and find frequent itemsets with a minimum support of 0.3 using the apriori function from mlxtend.
The output of this code will show the frequent itemsets and their corresponding support counts. Since the iris dataset is relatively small, we only find a single frequent itemset −
Output
support itemsets 0 0.333333 (target=0) 1 0.333333 (target=1) 2 0.333333 (target=2)
This indicates that 33% of the transactions in the dataset contain both a petal_length value of 1.4 and a target label of 0 (which corresponds to the setosa species in the iris dataset).
The Apriori algorithm is widely used in market basket analysis to identify patterns in customer purchasing behavior. For example, a retailer might use the algorithm to find frequently purchased items that can be promoted together to increase sales. The algorithm can also be used in other domains such as healthcare, finance, and social media to identify patterns and generate insights from large datasets.