Klaszteres

Klaszteres is the Hungarian term for clustering or cluster analysis, referring to the process of organizing a set of objects into groups, or clusters, so that objects within the same cluster are more similar to each other than to objects in other clusters. It is widely used in statistics, data mining, and related fields to uncover structure in unlabeled data and to reduce complexity by grouping similar items.

Clustering is typically an unsupervised learning task: there are no predefined labels guiding the grouping. The main goals are to maximize intra-cluster similarity and to minimize inter-cluster similarity. Practitioners choose data representations, similarity or distance measures, and clustering algorithms to suit the data and the research question.

Common clustering methods include k-means, which partitions data into a predetermined number of clusters based on centroids; hierarchical clustering, which builds a nested tree of clusters; DBSCAN and other density-based methods, which form clusters based on data density; Gaussian mixture models, which assume data are generated from a mix of statistical distributions; and spectral clustering, which uses eigenvectors of a similarity matrix to reduce dimensionality before clustering.

Applications of klaszteres span many domains, such as market segmentation, document and image organization, bioinformatics, social sciences, and anomaly detection. Evaluation of clustering results relies on internal criteria (e.g., cohesion and separation) and, when available, external criteria using ground truth labels (e.g., adjusted Rand index or silhouette scores).

Etymologically, the term is derived from the English word cluster and is adapted into Hungarian as klaszter, with -es forming an adjectival or participial derivative.