Titlebook: Between Data Science and Applied Data Analysis; Proceedings of the 2 Martin Schader,Wolfgang Gaul,Maurizio Vichi Conference proceedings 200

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書目名稱Between Data Science and Applied Data Analysis影響因子(影響力)




書目名稱Between Data Science and Applied Data Analysis影響因子(影響力)學科排名




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書目名稱Between Data Science and Applied Data Analysis網(wǎng)絡公開度學科排名




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書目名稱Between Data Science and Applied Data Analysis被引頻次學科排名




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書目名稱Between Data Science and Applied Data Analysis年度引用學科排名




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書目名稱Between Data Science and Applied Data Analysis讀者反饋學科排名

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Cluster- and Discriminant Analysis With Both Metric and Categorial Datapological properties of the subset of binary data of the introduced topological data model. The given example from internal medicine also contains variable selection..The computations are carried out in a SAS environment. The programs can be requested by the authors.

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https://doi.org/10.1007/978-3-030-38822-5 predictors is discussed. Specific attention is devoted to what is termed a ‘reversal’ when dealing with two binary (categorical) predictor variables. A proposal using biplot methodology is made for dealing with this problem.

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Joana Hora,Teresa Galv?o,Ana Camanhoum, and the trees can be used for simplified representation and visualization of the observations. We compare the accuracy and speed of different approximations, apply the method to real-world data sets and compare its computational complexity to published algorithms.

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Alexander V. Lagerev,Igor A. Lagerevlity clusters. We show how the scalability to large sizes is achieved using those advanced linear algebra techniques. These techniques also lead to an alternate representation of the dataset which is close enough to the original for the purposes of clustering while occupying a much smaller memory footprint.

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https://doi.org/10.1007/978-3-319-14768-0LS proposed by (.) but used in the discrimination context. The second proposal, in the same framework, leads to consider the PLS Redundancy Analysis proposed by (.) by using suitable metrics. Some examples of data treatment are given.

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