MATLAB Implementation of Data Mining Algorithms

MATLAB provides multiple data mining algorithms for implementation. One particularly practical algorithm is the Classification and Regression Trees (CART) decision tree, which is effective for classification problems and capable of identifying multiple classes. The decision tree algorithm represents a widely-used classification approach that learns patterns from training data and applies learned rules to classify new data instances. Through MATLAB's implementation, users can employ functions like 'fitctree' for creating classification trees, with parameters controlling tree depth and splitting criteria. The algorithm works by recursively partitioning data based on feature thresholds that maximize information gain or minimize Gini impurity. By utilizing decision tree algorithms, data analysts can better comprehend data patterns, perform insightful data analysis, and make accurate classification predictions while visualizing the decision-making process through tree structures.