This text provides an introduction to clustering methods, including both hierarchical and non-hierarchical methods. It shows how clustering can be used to interpret large quantities of analytical data and it discusses the relation of clustering to other pattern recognition technologies. A two-level approach is used 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. A worked example and a list of computer packages are included.
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