Clustering Algorithms
Clustering algorithms are unsupervised machine learning techniques that group similar data points together based on their features, without predefined labels. They identify patterns and structures in data by partitioning it into clusters, where points within a cluster are more similar to each other than to those in other clusters. Common applications include customer segmentation, anomaly detection, and image compression.
Developers should learn clustering algorithms when working with unlabeled data to discover hidden patterns, reduce dimensionality, or preprocess data for downstream tasks. They are essential in fields like data mining, bioinformatics, and recommendation systems, where grouping similar items can reveal insights or improve model performance. For example, K-means is widely used for market segmentation, while DBSCAN excels in identifying outliers in spatial data.