MATLAB Implementation of K-Means Clustering Algorithm with Iris Dataset Demo

The K-means algorithm is a classic clustering analysis method widely used in data mining and pattern recognition fields. Implementing this algorithm in MATLAB environment leverages its powerful matrix computation capabilities, making algorithm execution significantly more efficient. The implementation presented here utilizes the famous Iris dataset as test data, which contains 150 samples with 4 feature dimensions each. The core algorithm implementation involves several key steps: first, randomly selecting K initial centroids as cluster centers, then continuously optimizing these center positions through an iterative process. In each iteration, the code calculates distances between all data points and each centroid, assigning data points to the nearest cluster based on the minimum distance principle. The algorithm then recalculates centroid positions for each cluster as new centers. This iterative process continues until centroid positions show no significant changes or the preset maximum iteration count is reached. The MATLAB implementation efficiently handles these computations using vectorized operations and built-in functions like pdist2 for distance calculations. When testing with the Iris dataset, special attention must be paid to data preprocessing. Since different feature dimensions may have varying scales and value ranges, standardization such as z-score normalization is typically required to ensure balanced contributions from all dimensions to distance calculations. The implementation includes a preprocessing module that automatically handles feature scaling using MATLAB's zscore function. Experimental results demonstrate that this MATLAB implementation consistently clusters the Iris dataset into three groups, showing excellent alignment with the three flower species categories inherent in the dataset. In practical applications, the choice of K value significantly impacts clustering results. For the Iris dataset, the known category count is 3, making K=3 a reasonable choice. However, for unknown data, optimal K values may need determination through methods like the elbow method or silhouette coefficient. The algorithm implementation incorporates a mechanism for multiple random initializations followed by selection of the best result, effectively reducing random influences from initial centroid selection on final outcomes. This is achieved through a wrapper function that runs the core k-means algorithm multiple times and selects the configuration with the lowest within-cluster sum of squares.