Clustering is an unsupervised learning technique that involves grouping a set of data points into clusters such that points in the same cluster are more similar to each other than to points in other clusters​. Unlike classification, clustering operates on unlabeled data – the algorithm tries to discover inherent groupings or structure in the data without any ground truth labels. The goal is to maximize intra-cluster similarity (data points within a cluster should be as alike as possible) and maximize inter-cluster difference (distinct clusters should be well separated or different in characteristics).A classic example is clustering customers based on their purchase behavior: the algorithm might find one cluster of customers who buy mainly baby products, another cluster who buy luxury items, and so on – without having been told what those groups are beforehand. The “similarity” is defined via a distance or similarity measure (Euclidean distance is common for numeric data, but other measures or learned embeddings can be used). There are many clustering algorithms, each with different assumptions about cluster shape or formation: K-means clustering assumes clusters are roughly spherical in the feature space and partitions data into $k$ clusters by iteratively assigning points to the nearest cluster centroid and updating centroids; hierarchical clustering builds a tree of clusters by either successively merging the closest clusters (agglomerative) or splitting clusters (divisive), which allows one to choose a clustering at any level of granularity; DBSCAN defines clusters as areas of high density and can find arbitrarily shaped clusters while marking outliers as noise (it’s good for datasets with irregular cluster shapes); Gaussian mixture models assume data is generated from a mixture of Gaussian distributions and use statistical inference (EM algorithm) to soft-cluster points. Despite different approaches, the common theme is that clustering algorithms try to capture the natural structure in data.Clustering is often used for exploratory data analysis – to discover patterns that weren’t immediately apparent. For example, in biology, gene expression data might be clustered to find groups of genes with similar expression profiles (perhaps indicating co-regulation). In image processing, one might cluster pixel colors to compress images (color quantization) or cluster images in an unsupervised way to organize a photo collection by content. It’s also used in anomaly detection (points that don’t fit well into any cluster can be considered anomalies). One challenge with clustering is evaluating the results: since there are no true labels, validation uses metrics like silhouette score or Davies–Bouldin index (which assess cohesion and separation of clusters), or one uses domain knowledge to interpret clusters. Another challenge is that clustering can be sensitive to scaling of features and the choice of distance metric. Often, some preprocessing (like PCA for dimensionality reduction or feature normalization) is done to make clustering more effective. Overall, clustering is a powerful tool to let the data speak for itself by revealing potential groupings that can lead to insights or serve as a preprocessing step for other tasks (e.g., cluster then classify, or initialize labels via clustering).