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SNOB is Chris Wallace's computer program for unsupervised classification of multivariate data. The classification problem is sometimes called clustering, mixture modelling or numerical taxonomy. SNOB uses the Minimum [Message|Description] Length [Encoding] (MML|MDL) principle to decide upon the best classification of the data. MML encoding is a realisation of Ockham's razor. SNOB is very efficient and can classify many tens of thousands of "things" quickly, where each thing can have tens of "attributes" (variables). An attribute can be continuous (real-valued) or discrete (multi-state). A "class" is defined by distributions on one or more, but not necessarily all, attributes. The number of classes, the classes, their defining attributes and distributions, and class memberships are all inferred by SNOB. Chris wrote a later, more powerful version, 'Factor Snob', which includes hierarchical classes, and factor analysis. Selected Bibliography:
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