Titlebook: Advances in Intelligent Data Analysis VIII; 8th International Sy Niall M. Adams,Céline Robardet,Jean-François Bouli Conference proceedings

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书目名称Advances in Intelligent Data Analysis VIII影响因子(影响力)学科排名




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书目名称Advances in Intelligent Data Analysis VIII网络公开度学科排名




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书目名称Advances in Intelligent Data Analysis VIII被引频次学科排名




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书目名称Advances in Intelligent Data Analysis VIII年度引用学科排名




书目名称Advances in Intelligent Data Analysis VIII读者反馈




书目名称Advances in Intelligent Data Analysis VIII读者反馈学科排名

FUME

Electrolysis

978-3-642-03914-0Springer-Verlag Berlin Heidelberg 2009

Spinal-Fusion

天然热喷泉

Petteri Kaski,Patric R.J. Östergårdrely random configurations, is a powerful method to unravel their underlying interactions. I study here the spatial organization of retail commercial activities. From pure location data, network analysis leads to a community structure that closely follows the commercial classification of the US Depa

漂浮

Petteri Kaski,Patric R.J. Östergård multi-dimensional data streams. We use relative entropy, also known as the Kullback-Leibler distance, to measure the statistical distance between two distributions. In the context of a multi-dimensional data stream, the distributions are generated by data from two sliding windows. We maintain a sam

断断续续

的染料

Definitions and Basic Properties,hich are then scaled separately. The MDS items can be split into sub-problems using demographic variables in order to choose the sections of the data with optimal and sub-optimal mappings. The lower dimensional solutions from the scaled sub-problems are recombined by taking sample points from each s

失误

Easily Reconstructable Functions,in an optimal clustering for the considered data. The clustering aggregation concept tries to bypass this problem by generating a set of separate, heterogeneous partitionings of the same data set, from which an aggregate clustering is derived. As of now, almost every existing aggregation approach co

Pander

Martin Holeňa,Petr Pulc,Martin Kopp the human perception of time series. A time series and its translated copy appear dissimilar under the Euclidean distance (because the comparison is made pointwise), whereas a human would perceive both series as similar. As the human perception is tolerant to translational effects, using the cross