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Numerical Taxonomy: The Principles and Practice of Numerical Classification Peter H. A. Sneath
Publisher: W H Freeman & Co (Sd)

FEMS Microbiology Reviews 25, 39-67. Sneath PHA, Sokal RR: Numerical taxonomy — the principles and practice of numerical classification. Numerical taxonomy: the principles and practice of numerical classification. Actually phenetics is simply one possible position on classification, but it is considered an insult by many to call someone a pheneticist. Numerical taxonomy (also called computer taxonomy, phenetics, or taxometrics) became widely used in the 1960s. Sokal RR, Sneath PHA: Numerical Taxonomy: the Principles and Practice of Numerical Classification. Tudhope D., Taylor C., Beynon-Davies P. Partridge TR, Dallwitz MJ, Watson L: A primer for the DELTA system. Sneath, became known as numerical taxonomy. Sneath PHA, Sokal R (1973) Numerical taxonomy: the principles and practices of classification. WH Freeman and Co., San Francisco, California, USA. During his long career he made distinguished contributions to evolutionary biology, systematics, human population genetics, and statistics, and generations of biologists have learned the principles and practices of statistical inference from The latter, which he pioneered with P.H.A. Emphasizing no particular mechanism of evolution, cladistics as a classification schema lies largely separate from much of the debate between those who favor natural selection and those who favor intelligent design. The classification of Taxonomic units into various groups by numerical methods is called “Numerical Taxonomy”. San Franscisco: WH Freeman & Co; 1973. Numerical Taxonomy: The Principles and Practice of Numerical Classification. San Francisco: W H Freeman; 1973. Sneath PHA, Sokal RR: Numerical taxonomy: the principles and practice of numerical classification. San Francisco: W H Freeman; 1973:573. The species concept for prokaryotes. The publication of pioneer work of Sokal and Sneath (1963) “Principle of Numerical Taxonomy” was the first comprehensive Although intended as an objective classification method, in practice the choice and implicit weighing of characteristics is, of course, influenced by available data and research interests of the investigator.