A practical, user-oriented introduction to clustering methods, including both hierarchical and non-hierarchical methods. Shows how clustering can be used to interpret large quantities of analytical data and discusses the relation of clustering to other pattern recognition techniques. Uses a two-level approach to provide both a qualitative understanding of the philosophy, advantages, and disadvantages of clustering and a quantitative understanding for readers who want a strong mathematical background. Includes a worked example and a list of computer packages.
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