klastrowania

Klastrowanie, or clustering, is a set of techniques used to group objects into clusters so that objects within the same cluster are more similar to each other than to objects in different clusters. It is typically an unsupervised learning task, meaning there is no labeled target variable accompanying the data. The goal is to discover natural groupings and the overall structure of a dataset.

In data analysis, clustering helps identify patterns, segment populations, summarize large datasets, and support exploratory data analysis. It is used in market research, document and image analysis, bioinformatics, customer segmentation, anomaly detection, and recommender systems. In information technology, the term also describes clustering of computing resources—groups of servers or services designed for high availability, load balancing, and parallel processing.

Common approaches include partitioning methods (for example, k-means and k-medoids), which divide data into a predefined number of clusters; hierarchical methods (agglomerative or divisive), which create nested clusters; density-based methods (such as DBSCAN and OPTICS), which form clusters based on data density and can discover arbitrarily shaped groups; and model-based methods (like Gaussian mixture models), which assume data are generated from probabilistic models. Distance or similarity measures (Euclidean, Manhattan, cosine similarity, etc.) play a central role in most algorithms.

Evaluation of clustering results is challenging because there is often no ground truth. Internal indices like the silhouette score or Davies-Bouldin index are common, while external validation is possible when labeled data are available. Practical considerations include scaling and standardizing features, choosing the number of clusters, initializing algorithms, and interpreting cluster meaning in the domain context.