By Antonio Mucherino
Data Mining in Agriculture represents a complete attempt to supply graduate scholars and researchers with an analytical textual content on facts mining strategies utilized to agriculture and environmental comparable fields. This e-book offers either theoretical and useful insights with a spotlight on offering the context of every info mining process quite intuitively with considerable concrete examples represented graphically and with algorithms written in MATLAB®.
Examples and workouts with ideas are supplied on the finish of every bankruptcy to facilitate the comprehension of the fabric. for every facts mining procedure defined within the booklet editions and enhancements of the fundamental set of rules also are given.
Also through P.J. Papajorgji and P.M. Pardalos: Advances in Modeling Agricultural Systems, 'Springer Optimization and its purposes' vol. 25, ©2009.
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The acceptance strategy usually used is based on the Metropolis acceptance function . 5 Data mining and agriculture 19 t = t0 maxout = maximum allowed number of outer iterations nsteps = number of steps at constant temperature X = random starting solution nout = 0 while (f (X) not stable and nout ≤ maxout) nout = nout + 1 for k = 1, nsteps X(k) = random perturbation on X p = uniform random number in (0,1) if (A(X,X(k),t)) > p) then X = X(k) end if end for t = γ t, γ < 1 end while Fig. 4 The simulated annealing algorithm.
By applying interpolation or regression techniques, the hope is to find a function that is able to describe this phenomenon or process in general. Let us suppose that the quantity of water y in a certain soil is monitored during time x. Experimental analysis can be used for obtaining y at different times x, so that a set of points (x , y ) can be defined. As always in real life applications, the number of experiments is discrete and limited, whereas a general function able to relate each time x to a water level y is searched.
For more details about validation techniques, refer to Chapter 8. 3 Mining market data Data mining applied to finance is also referred to as financial data mining. Some of the most recent papers on this topic are , in which a new adaptive neural network is proposed for studying financial problems, and , in which stock market tendency is studied by using a support vector machine approach. In fact, in finance, one of the most important problems is to study the behavior of the market. The large number of stock markets provides a considerable amount of data every day in the United States only.