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.