Latent structureMultivariate analysis
Cluster Analysis
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.
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Sources
- Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913
- Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Related methods
Referenced by
Bayesian Cluster AnalysisBayesian Hierarchical ClusteringBayesian K-means clusteringDiscriminant AnalysisLatent Class AnalysisLCAMixture ModelingMultidimensional ScalingMultivariate Exploratory Quantitative ResearchRobust Hierarchical ClusteringRobust K-means ClusteringRobust Latent Class AnalysisRobust Multiple Correspondence AnalysisSingle-cell RNA-seq differential expression