K-Means Clustering
K-Means Clustering is an unsupervised machine learning algorithm used for partitioning data into K distinct, non-overlapping clusters based on feature similarity. It works by iteratively assigning data points to the nearest cluster centroid and updating centroids to minimize within-cluster variance. It is widely applied in data mining, pattern recognition, and customer segmentation for exploratory data analysis.
Developers should learn K-Means Clustering when dealing with unlabeled data to discover inherent groupings, such as in market segmentation, image compression, or anomaly detection. It is particularly useful for preprocessing data, reducing dimensionality, or as a baseline for more complex clustering methods, due to its simplicity and efficiency on large datasets.