Implementation of Data Mining Algorithm ID3 in MATLAB

This implementation presents the ID3 data mining algorithm using MATLAB. The ID3 algorithm is an information gain-based decision tree method that constructs classification and prediction models by analyzing dataset features and attributes. As a fundamental data mining technique, it effectively handles both classification and regression problems. Our MATLAB implementation leverages the language's robust data processing capabilities through custom code that calculates entropy, information gain, and recursively builds decision trees. The implementation follows key algorithmic steps: data preprocessing, feature selection based on maximum information gain, node splitting, and recursive tree generation until termination criteria are met. Core functions include calculating entropy for attribute subsets, determining optimal splitting points, and handling both categorical and numerical data through appropriate discretization methods. The code structure ensures efficient handling of large datasets while maintaining algorithm purity through proper pruning techniques and overfitting prevention mechanisms.