klaszteralapú
Klaszteralapú is a term used to describe approaches, methods, or analyses that rely on clustering as a core step in the analytical workflow. A klaszteralapú analysis organizes a set of objects into clusters based on similarity or distance, and uses the resulting cluster structure to guide further modeling, inference, or interpretation. The clustering step can employ various algorithms (for example, k-means, hierarchical clustering, DBSCAN) and requires choices of distance metrics and the number of clusters, parameters that influence the results.
In a klaszteralapú framework, subsequent analysis is often performed at the level of clusters rather than at
Advantages include data simplification, the ability to reveal structure not apparent from single observations, and potential
Related concepts include cluster analysis, unsupervised learning, and cluster-based inference methods such as cluster-level permutation tests.