Implementation of the Classic C-Means Algorithm in Artificial Intelligence

In this article, we discuss the implementation of the classic c-means algorithm in artificial intelligence. The c-means algorithm is a fundamental clustering analysis method that partitions data points into distinct groups. We focus specifically on implementing the c-means algorithm in MATLAB, where data storage using matrices proves significantly more efficient than implementation in VC environments. MATLAB's matrix operations provide an intuitive and concise approach for data representation and manipulation, greatly simplifying the c-means algorithm implementation. The implementation leverages MATLAB's built-in matrix computation capabilities, where we can efficiently handle cluster centroids initialization using random selection from the dataset. The core algorithm involves iteratively updating cluster assignments through Euclidean distance calculations between data points and centroids using matrix operations like 'pdist2'. Each iteration recalculates centroid positions by computing the mean of all points assigned to each cluster, which can be efficiently achieved using MATLAB's 'mean' function with logical indexing. Compared to VC implementations that require manual memory management and loop-based calculations, MATLAB's vectorized operations allow for concise code with functions like 'kmeans' (for basic implementation) or custom functions utilizing matrix multiplication and broadcasting for distance computations. The algorithm typically converges when centroid positions stabilize between iterations, which can be monitored using difference thresholds and while-loop structures. We will employ matrix-based data storage and provide a detailed, step-by-step explanation of implementing each phase of the c-means algorithm in MATLAB, including data preprocessing, centroid initialization, assignment updating, and convergence checking.