By Rainer Schmidt, Tina Waligora (auth.), Petra Perner (eds.)
The business convention on information Mining ICDM-Leipzig was once the 6th occasion in a chain of annual occasions which began in 2000. we're happy to notice that the subject facts mining with unique emphasis on real-world purposes has been followed via such a lot of researchers around the world into their examine paintings. We got 156 papers from 19 diversified international locations. the most issues are facts mining in medication and advertising, net mining, mining of pictures and signs, theoretical features of information mining, and facets of information mining that package a sequence of alternative info mining functions akin to intrusion detection, wisdom administration, production strategy keep an eye on, time-series mining and legal investigations. this system Committee labored tough so one can choose the easiest papers. The attractiveness cost was once 30%. these types of chosen papers are released during this court cases quantity as lengthy papers as much as 15 pages. furthermore we put in a discussion board the place paintings in growth was once offered. those papers are amassed in a distinct poster court cases quantity and convey once again the potentials and engaging advancements of knowledge mining for various purposes. 3 new workshops were proven in reference to ICDM: (1) Mass information research on photographs and signs, MDA 2006; (2) facts Mining for all times Sciences, DMLS 2006; and (3) facts Mining in advertising, DMM 2006. those workshops are constructing new themes for facts mining below the element of the specified program. we're happy to determine what number attention-grabbing advancements are happening in those fields.
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Additional info for Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 14-15, 2006. Proceedings
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2 Gene Entropy Estimation Our goal is to get the gene distribution function p ' ( x ) for gene x in the feature space to approximate the gene distribution function p (x) in the sequence space. How can we get the gene distribution on the SOM plane? We give the definitions about “hit” and “hit-rate” before we give the formal definition of gene distribution on the SOM plane. If a neuron j on the SOM plane is acknowledged as the best match unit (BMU) of sample/site xi in the gene x after the SOM mining, we say that the gene sample xi hits the neuron j .
In: Proc. 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2004) 737–742 6. : Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17 (2001) 1131–1142 7. : Feature selection for high-dimensional genomic microarray data. In: Proc. 18th International Conference on Machine Learning (2001) 601–608 8. : Relevance, redundancy and differential prioritization in feature selection for multiclass gene expression data.