Klasterizimin

Klasterizimin, often rendered in English as clustering, is a data analysis technique that aims to partition a set of objects into groups, or clusters, so that members within the same cluster are more similar to each other than to members of other clusters. It is a central method in unsupervised learning, where no predefined labels are available.

Clustering methods differ in how they define similarity and how they form clusters. Common families include partitional approaches such as k-means and k-medoids, hierarchical methods that build nested clusters, density-based algorithms like DBSCAN that identify dense regions, and model-based approaches such as Gaussian mixture models. Spectral clustering, which uses the eigenstructure of a similarity graph, is another widely used technique. Distance or similarity measures—such as Euclidean distance, Manhattan distance, or cosine similarity—and data normalization greatly influence results.

Evaluating clustering results is challenging because true labels may be unknown. Internal indices like the silhouette coefficient and the Davies-Bouldin index assess compactness and separation, while external indices (when ground truth is available) compare clusters to known classifications. Selecting the number of clusters (k) or determining appropriate parameters remains a key practical problem.

Applications of klasterizimin span market segmentation, image and text analysis, biology and genomics, anomaly detection, and pattern discovery in large datasets. Limitations include sensitivity to initialization, the curse of dimensionality, arbitrariness in choosing distance measures, and the fact that real data may not form well-separated clusters. Despite these challenges, clustering remains a foundational tool in exploratory data analysis.