So sánh phương pháp

	Mô hình hóa hỗn hợp	Phân tích cụm
Lĩnh vực	Thống kê	Thống kê
Họ	Latent structure	Latent structure
Năm ra đời	1894	1939–1967
Người khởi xướng	Karl Pearson	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means
Loại	Latent variable / density estimation	Unsupervised classification / grouping
Công trình gốc	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913
Tên gọi khác	finite mixture model, mixture distribution model, FMM, model-based clustering	clustering, unsupervised classification, data clustering, numerical taxonomy
Liên quan	6	5
Tóm tắt	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.	Cluster 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.