K-Means Clustering

Step (1): K-means clustering method starts with random points for the cluster centers.
Step (2): Cluster membership is assigned to each data point based on the distance to the nearest cluster center.
Step (3): Calculate the new cluster center by taking the mean of the data points that belongs to a cluster. Repeat this for each cluster.

Step (4): Repeat Steps (2) and (3) until the cluster centers are stable or below a given threshold.

Distance metrics to use: Euclidean distance and Mahalanobis distance.
Membership assignment: binary decision (0 or 1), soft decision (probability).