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

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書目名稱Advances in Intelligent Data Analysis VIII影響因子(影響力)




書目名稱Advances in Intelligent Data Analysis VIII影響因子(影響力)學(xué)科排名




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書目名稱Advances in Intelligent Data Analysis VIII網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Intelligent Data Analysis VIII被引頻次




書目名稱Advances in Intelligent Data Analysis VIII被引頻次學(xué)科排名




書目名稱Advances in Intelligent Data Analysis VIII年度引用




書目名稱Advances in Intelligent Data Analysis VIII年度引用學(xué)科排名




書目名稱Advances in Intelligent Data Analysis VIII讀者反饋




書目名稱Advances in Intelligent Data Analysis VIII讀者反饋學(xué)科排名

FUME

Electrolysis

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

Spinal-Fusion

天然熱噴泉

Petteri Kaski,Patric R.J. ?sterg?rdrely 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. ?sterg?rd 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

斷斷續(xù)續(xù)

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

失誤

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