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Titlebook: Advances in Data Mining - Theoretical Aspects and Applications; 7th Industrial Confe Petra Perner Conference proceedings 2007 Springer-Verl

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期刊全称Advances in Data Mining - Theoretical Aspects and Applications
期刊简称7th Industrial Confe
影响因子2023Petra Perner
视频videohttp://file.papertrans.cn/148/147671/147671.mp4
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Advances in Data Mining - Theoretical Aspects and Applications; 7th Industrial Confe Petra Perner Conference proceedings 2007 Springer-Verl
影响因子ICDM / MLDM Medaillie (limited edition) Meissner Porcellan, the “White Gold” of King August the Strongest of Saxonia ICDM 2007 was the seventh event in the Industrial Conference on Data Mining series and was held in Leipzig (www.data-mining-forum.de). For this edition the Program Committee received 96 submissions from 24 countries (see Fig. 1). After the peer-review process, we accepted 25 high-quality papers for oral presentation that are included in this proceedings book. The topics range from aspects of classification and prediction, clustering, Web mining, data mining in medicine, applications of data mining, time series and frequent pattern mining, and association rule mining. Germany 9,30% 4,17% China 9,30% 1,04% 6,98% 3,13% South Korea Czech Republic 6,98% 3,13% USA 6,98% 2,08% 4,65% 2,08% UK Portugal 4,65% 2,08% Iran 4,65% 2,08% India 4,65% 2,08% Brazil 4,65% 1,04% Hungary 4,65% 1,04% Mexico 4,65% 1,04% Finland 2,33% 1,04% Ireland 2,33% 1,04% Slovenia 2,33% 1,04% France 2,33% 1,04% Israel 2,33% 1,04% Spain 2,33% 1,04% Greece 2,33% 1,04% Italy 2,33% 1,04% Sweden 2,33% 1,04% Netherlands 2,33% 1,04% Malaysia 2,33% 1,04% Turkey 2,33% 1,04% Fig. 1. Distribution of papers among c
Pindex Conference proceedings 2007
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https://doi.org/10.1007/b138249ature is the formulation of mixed integer programming problems aimed at deriving optimal separating hyperplanes with minimum empirical error and maximum generalization capability. A new family of discrete SVM is proposed in this paper, for which the hyperplane establishes a variable softening of the
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Categories for the Working Mathematician to use common software platform is available. .- Data .ualisation by Space .uction presented here, aims to be such a tool for data classification and clustering. It allows the quick application of Principal Component Analysis, Nonlinear Principal Component Analysis, Multi-dimensional Scaling (class
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