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

The 32 revised complete papers provided have been conscientiously reviewed and chosen from one hundred thirty submissions. The papers are geared up in topical sections on information mining in drugs and agriculture, info mining in advertising and marketing, finance and telecommunication, information mining in procedure regulate, and society, info mining on multimedia facts and theoretical elements of knowledge mining.

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1, pp. 1,∗ and J. Ricardo Pérez-Correa2 1 Escuela de Ingeniería Industrial. Facultad de Ciencias Económicas y Administrativas. Universidad de Valparaíso, Las Heras Nº6. Valparaíso, Chile. : +56-32-2507788; Fax: +56-32-2507958 1 Departamento de Ingeniería Química y Ambiental, Universidad Técnica Federico Santa María Av. España 1680, Casilla 110-V, Valparaíso, Chile. cl 2 Departamento de Ingeniería Química y Bioprocesos, Escuela de Ingeniería, Pontificia Universidad Católica de Chile. cl Abstract.

Classification results of clustering K-means using 3 clusters and 3, 5 or 8 PC, for dataset A # PC Blue Normal Problematic 3 2, 5, 6, 7, 10, 11, 12, 8 13, 14, 15, 16, 17, 18, 20, 22 5 2, 5, 6, 7, 10, 11, 12, 8 13, 14, 16, 17, 18, 20, 22 2, 5, 6, 8 10, 11, 12, 8 13, 14, 16, 17, 18, 20, 22 Normal 1, 3, 4, 7, 8, 9 1, 3, 4, 7, 8, 9 1, 3, 4, 7, 8, 9 Cluster Red Problematic 6, 10, 12, 13, 15, 17, 18, 19, 20, 21, 23, 24 6, 10, 12, 13, 14, 15, 17, 19, 21, 23, 24 6, 10, 12, 13, 14, 15, 17, 18, 19, 20, 21, 23, 24 Pink Normal Problematic 1, 2, 3, 4, 10, 14, 15, 7, 8, 9 20, 23, 24 1, 2, 3, 4, 10, 15, 18, 6, 7, 8, 9 19, 20, 23, 24 1, 2, 3, 4, 10, 15, 20, 7, 8, 9 21, 23, 24 Table 2.

In this work we study the impact that the number of principal components (PCs) has on the classification of problematic wine fermentations using clustering k-means. 1 Datasets Between 30 and 35 samples were collected from each of 24 normal and abnormal operation industrial wine fermentations of Cabernet-Sauvignon. Each sample was analyzed for sugars, alcohols, and organic acids [8]. In addition, nitrogen-rich compounds were analyzed, though these measurements were less reliable. In all, 28 compounds were analyzed per sample resulting in 22,000 measurements.

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