Data mining and exploration techniques

Publisher Summary The data mining and exploration methods introduce algorithms that automate predictor and equation selections. This chapter describes three methods: artificial neural networks, group method of data handling (GMDH), and the regression tree that have recently been used in the pedotransfer function (PTF) development. Each of these methods has its advantages and disadvantages. For example, the advantage of regression trees is the transparency of results, whereas the advantage of neural networks is the ability to mimic practically any relationship. The disadvantage of all these techniques as compared to statistical regression is the heuristic element involved so that the rigorous statistical judgment is hard to make. The three techniques practically produce identical PTF accuracy. The database exploration is a useful step that may generate PTFs that are either sufficient for the intended application or may suggest further applications of more rigorous or more flexible PTF-building techniques.

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