Data Mining MATLAB Source Code Collection

This MATLAB source code collection for data mining incorporates several classic algorithms from the machine learning domain, such as ID3, C4.5, NN, CARD, and EM. These algorithms assist users in conducting comprehensive data mining and analysis tasks. The ID3 algorithm is a decision tree algorithm that performs classification and prediction based on data attributes. Implementation typically involves calculating information gain to select optimal splitting attributes at each node. The C4.5 algorithm serves as an enhanced version of ID3, capable of handling continuous attributes and missing values through gain ratio calculations and sophisticated pruning techniques. The NN (Neural Network) algorithm simulates human neuron functionality using weighted connections and activation functions, applicable to both classification and regression problems through forward propagation and backpropagation training. The CARD algorithm is a classification rule discovery method that automatically extracts classification rules from datasets using rule induction techniques and coverage measurements. The EM (Expectation-Maximization) algorithm is a clustering approach that partitions data points into distinct clusters through iterative expectation and maximization steps, particularly effective for Gaussian mixture models. By leveraging these fundamental algorithms, users can effectively interpret and analyze complex datasets to extract valuable insights and patterns. The MATLAB implementations provide practical examples of algorithm application with clear code structure and parameter configuration options.