k-Means Algorithm Implementation with Custom Data Samples and Cluster Centers

k-means algorithm implementation featuring custom data samples and cluster centers, thoroughly debugged and visualized with clear graphical representations

Fuzzy C-Means Clustering Algorithm

The Fuzzy C-Means Algorithm (FCM), also known as Fuzzy C-Means Clustering (FCMA), is the most widely adopted and successful approach among fuzzy clustering techniques. This algorithm optimizes an objective function to compute membership degrees for each data point relative to all cluster centers, enabling automatic classification of sample data. Key implementation aspects include iterative centroid updates using weighted averages and membership recalculation based on distance metrics.