Download Advances in Data Mining. Applications and Theoretical by Claus Weihs, Gero Szepannek (auth.), Petra Perner (eds.) PDF

By Claus Weihs, Gero Szepannek (auth.), Petra Perner (eds.)

This publication constitutes the refereed complaints of the ninth commercial convention on information Mining, ICDM 2009, held in Leipzig, Germany in July 2009.

The 32 revised complete papers awarded have been rigorously reviewed and chosen from a hundred thirty submissions. The papers are equipped in topical sections on info mining in drugs and agriculture, information mining in advertising, finance and telecommunication, info mining in technique keep watch over, and society, info mining on multimedia info and theoretical facets of information mining.

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Extra resources for Advances in Data Mining. Applications and Theoretical Aspects: 9th Industrial Conference, ICDM 2009, Leipzig, Germany, July 20 - 22, 2009. Proceedings

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This is especially useful when new data have to evaluated using one of the presented models. 2 Article Structure Section 2 lays out the data sets that this work builds upon. The attributes and their properties will be presented shortly. Section 3 presents four selected regression techniques from the data mining area which will be used for yield prediction. The free model parameters will be described. Section 4 shows the results from the modeling steps and provides answers to the aforementioned research questions.

As mentioned in [8], the value of ε is inversely proportional to the number of support vectors. An adequate setting of C and ε is necessary for a suitable solution to the regression problem. Furthermore, K(xi , x j ) is known as a kernel function which allows to project the original data into a higher-dimensional feature space where it is much more likely to be linearly separable. Some of the most popular kernels are radial basis functions (equation 4) and a polynomial kernel (equation 5): − ||x−xi ||2 K(x, xi ) = e 2σ 2 K(x, xi ) = ( x, xi + 1)ρ (4) (5) The parameters σ and ρ have to be determined appropriately for the SVM to generalize well.

Hence, here it is assumed that one hidden layer is sufficient to approximate the underlying function sufficiently well. For a more detailed and formal description of MLP neural networks, it is referred to [9] or [10]. Once the number of hidden layers of the network is fixed, there remain a few parameters to be determined. Similar to the remaining modeling techniques, those are usually determined experimentally. This also means that often a large parameter space has to be searched. For standard MLP networks, the size of the hidden layer, the learning rate, the activation function and the minimum gradient are the most important parameters that have to be set.

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