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
Common approaches include partitioning methods (for example, k-means and k-medoids), which divide data into a predefined
Evaluation of clustering results is challenging because there is often no ground truth. Internal indices like