By Claus Weihs, Gero Szepannek (auth.), Petra Perner (eds.)
This booklet constitutes the refereed court cases of the ninth business convention on facts Mining, ICDM 2009, held in Leipzig, Germany in July 2009.
The 32 revised complete papers provided have been rigorously reviewed and chosen from one hundred thirty submissions. The papers are geared up in topical sections on info mining in drugs and agriculture, facts mining in advertising and marketing, finance and telecommunication, info mining in strategy keep an eye on, and society, facts mining on multimedia facts and theoretical features of knowledge mining.
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Additional resources for Advances in Data Mining. Applications and Theoretical Aspects: 9th Industrial Conference, ICDM 2009, Leipzig, Germany, July 20 - 22, 2009. Proceedings
The impact that the number of PCs has on the classification is analyzed here. Previous works observed that classifications with the procedure described above using measurements from the first three days of fermentation (77 samples) were similar to those obtained with the entire data set (570 samples) . Hence, in this study we included only samples of the first three days of fermentation. 3 Results Here, fermentations that finished with more than 2 g/L of sugar or lasted more than 13 days were arbitrarily defined as problematic.
3 Future Work One aspect that should be considered in future work is the understandability of the model. While regression trees would be the easiest to understand, they bear the burden of providing worse results compared to SVR. There has already been quite a lot of work towards understanding the inner workings of an MLP, but it remains one of the more intransparent models. The same holds for SVR, but there has been some work using the support vectors for visualization, such as [33,15]. Rather than selecting one of the four presented models, there might be a certain combination of models that performs better than a single one.
399–408. Springer, Heidelberg (2007) 18. : “Red edge” optical properties of corn leaves from different nitrogen regimes. In: 2002 IEEE International Geoscience and Remote Sensing Symposium, vol. 4, pp. 2208–2210 (2002) 19. : Machine Learning. McGraw-Hill Science/Engineering/Math (March 1997) 20. : Nitrogen Management for Intensively Grown Arable Crops and Field Vegetables, ch. 7, pp. 295–326. CRC Press, Haren (1995) 21. : Combining regression trees and radial basis function networks. International Journal of Neural Systems 10 (1999) 22.