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

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

This booklet constitutes the refereed complaints of the ninth business convention on info Mining, ICDM 2009, held in Leipzig, Germany in July 2009.

The 32 revised complete papers offered have been rigorously reviewed and chosen from one hundred thirty submissions. The papers are equipped in topical sections on info mining in medication and agriculture, facts mining in advertising, finance and telecommunication, information mining in procedure keep an eye on, and society, info mining on multimedia info and theoretical facets of knowledge mining.

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Extra info 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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However, it would be quite interesting to try some non-linear dimensionality reduction algorithms such as Isomap or autoencoders optimized with ADATE. Even if one needs to elaborate on modifications of the present e-nose system, one should, indeed, recall that it was originally designed to detect various explosives. From this point of view the “nose is doing quite well”. References 1. : Development of Electronic Nose for Diagnosis of Lung Cancer at Early Stage. In: 5th Conference on Information Technology and Application in Biomedicine, Shenzhen, China, pp.

Inductive functional programming using incremental program transformation. Artificial Intelligence 1, 55–83 (1995) 13. : Automatic Design of Pulse Coupled Neurons for Image Segmentation. Neurocomputing - Special Issue for Vision Research (2008) 14. : Machine Learning. , New York (1997) 15. : A Flexible Electronic Nose for Ovarian Carcinoma Diagnosis in Real Time. Accepted to IEEE NPSS Real Time Conference, Beijing, China (2009) Data Mining of Agricultural Yield Data: A Comparison of Regression Models Georg Ruß Otto-von-Guericke-Universit¨at Magdeburg Abstract.

2 Vegetation – REIP 32, REIP 49 The red edge inflection point (REIP) is a second derivative value calculated along the red edge region of the spectrum, which is situated from 680 to 750nm. Dedicated REIP sensors are used in-season to measure the plants’ reflection in this spectral band. g. [18]), the REIP value allows for deducing the plants’ state of nutrition and thus, the previous crop growth. For further information on certain types of sensors and a more detailed introduction, see [13] or [32].

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