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Klaster analiza×Modeliranje smjesa×
PodručjeStatistikaStatistika
ObiteljLatent structureLatent structure
Godina nastanka1939–19671894
TvoracRobert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-meansKarl Pearson
VrstaUnsupervised classification / groupingLatent variable / density estimation
Temeljni izvorEveritt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
Drugi naziviclustering, unsupervised classification, data clustering, numerical taxonomyfinite mixture model, mixture distribution model, FMM, model-based clustering
Srodne56
SažetakCluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data.Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.
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ScholarGateUsporedite metode: Cluster Analysis · Mixture Modeling. Preuzeto 2026-06-15 s https://scholargate.app/hr/compare