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MULTIVARIATE MIXTURE OF NORMALS WITH UNKNOWN NUMBER OF COMPONENTS: AN APPLICATION TO CLUSTER NEOLITHIC CERAMICS FROM AEGEAN AND ASIA MINOR USING PORTABLE XRF *
Author(s) -
PAPAGEORGIOU IOULIA,
LIRITZIS IOANNIS
Publication year - 2007
Publication title -
archaeometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.716
H-Index - 67
eISSN - 1475-4754
pISSN - 0003-813X
DOI - 10.1111/j.1475-4754.2007.00336.x
Subject(s) - principal component analysis , cluster analysis , multivariate statistics , hierarchical clustering , provenance , context (archaeology) , compositional data , chemometrics , mixture model , data set , computer science , exploratory data analysis , archaeological science , curse of dimensionality , data mining , statistics , archaeology , mathematics , artificial intelligence , geology , geography , geochemistry , machine learning
Multivariate techniques and especially cluster analysis have been commonly used in archaeometry. Exploratory and model‐based techniques of clustering have been applied to geochemical (continuous) data of archaeological artefacts for provenance studies. Model‐based clustering techniques such as classification maximum likelihood and mixture maximum likelihood have been used to a lesser extent in this context and, although they seem to be suitable for such data, they either present practical difficulties—such as high dimensionality of the data—or their performance gives no evidence that they are superior to standard methods. In this paper standard statistical methods (hierarchical clustering, principal components analysis) and the recently developed model‐based multivariate mixture of normals with an unknown number of components, are applied and compared. The data set provides chemical compositions of 188 ceramic samples derived from the Aegean islands and surrounding areas.