Summary

This article introduced DM, the central activity in the process of KDD, which is concerned with finding patterns in data. The different types of patterns considered in DM include patterns, trees, and rules. DM algorithms search the space of possible patterns of a given type to find patterns that are valid in a given set of data (i.e., that fit the given data set well).

It also gave an overview of KDD applications in environmental sciences, complemented with a sample of case studies. DM has been successfully applied to a number of problems in ecology and environmental sciences in general. Typical success stories involve the use of DM to construct habitat-suitability models or population dynamics models. DM has also been used successfully in environmental epidemiology, environmental monitoring (including remote sensing approaches), and environmental toxicology.

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