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 the level of individual observations. This can mean describing cluster characteristics, performing hypothesis tests on cluster-level statistics, or building models that use cluster membership as predictors or features. Such approaches are common in data mining, machine learning, bioinformatics, market research, and neuroimaging, among others.

Advantages include data simplification, the ability to reveal structure not apparent from single observations, and potential reductions in dimensionality and multiple testing burden. Limitations include sensitivity to the chosen clustering method and parameters, potential instability of clusters, and the risk of overgeneralization from cluster-level results to individuals.

Related concepts include cluster analysis, unsupervised learning, and cluster-based inference methods such as cluster-level permutation tests. The term is used in Hungarian-language technical literature to describe methods that emphasize the role of clustering in the analytical workflow.