Association Rule Mining and Data Analysis MATLAB Toolbox (ARMDA)

Association rule mining is a crucial technique in data mining, primarily used to discover interesting associations or correlations between items in large-scale datasets. ARMDA (Association Rule Mining and Data Analysis) is a specialized toolbox designed for the MATLAB environment that simplifies the process of extracting association rules from transactional data, making it particularly suitable for market basket analysis in retail, e-commerce, and related fields.

Core functionalities of this toolbox include: Data Preprocessing: Supports transformation of raw transactional data into mining-ready formats such as transaction lists or binary matrices through functions like data_binarize() that convert categorical data into numerical representations. Rule Generation: Efficiently extracts frequent itemsets using classical algorithms (e.g., Apriori with its downward closure property or FP-Growth with its tree-based compression) and generates association rules (e.g., "customers who buy A are likely to buy B") through functions like apriori_miner() that implement the candidate generation-and-test approach. Metric Calculation: Automatically computes key metrics including Support (frequency of co-occurrence), Confidence (conditional probability), and Lift (measure of rule interestingness) using evaluate_rules() function, enabling users to filter meaningful rules based on statistical significance. Visualization Analysis: Provides intuitive graphical outputs such as rule network diagrams using graph() functions or heatmaps via heatmap() visualization, facilitating quick identification of strong association patterns through interactive plotting capabilities.

ARMDA's key advantage lies in its seamless integration with the MATLAB ecosystem. Users can directly call toolbox functions and combine them with other data analysis workflows (such as clustering with cluster() functions or classification using classify() methods) without switching programming environments. For scenarios requiring rapid validation of business hypotheses (e.g., product recommendation strategies), it significantly reduces implementation complexity by providing ready-to-use functions like generate_recommendations() that interface with MATLAB's built-in statistical and machine learning libraries.