Titlebook: Classification and Data Mining; Antonio Giusti,Gunter Ritter,Maurizio Vichi Conference proceedings 2013 Springer-Verlag Berlin Heidelberg

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書目名稱Classification and Data Mining影響因子(影響力)




書目名稱Classification and Data Mining影響因子(影響力)學(xué)科排名




書目名稱Classification and Data Mining網(wǎng)絡(luò)公開度




書目名稱Classification and Data Mining網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Classification and Data Mining被引頻次




書目名稱Classification and Data Mining被引頻次學(xué)科排名




書目名稱Classification and Data Mining年度引用




書目名稱Classification and Data Mining年度引用學(xué)科排名




書目名稱Classification and Data Mining讀者反饋




書目名稱Classification and Data Mining讀者反饋學(xué)科排名

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https://doi.org/10.1007/978-3-319-79029-9Test. A cut-off point separating the outliers from the other observations is identified through a graphical analysis of the information collected at each step of the Forward Search procedure; the Robust Forward . is the value of the classical . statistic at the cut-off point.

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Security Problem and Solutions to M-commercedata is produced, it is easy to classify the rest of the data with reliable and stable results. The good performance of the approach is shown on a complex dataset coming from international trade data.

homeostasis

Yimeei Guo,Dongsheng Yan,Weiwan Zhang in such a way that they break the independence in the table, but still they are not large enough to be easily spotted without statistical analysis. We study the change in the correspondence analysis row and column coordinates caused by the outliers and perform numerical analysis of the outlier coordinates.

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Timothy F. Slater,Coty B. Tatgeonsiderations. In both cases, we discuss the framework of CUB models introduced to analyse the ordinal responses by which people express their opinions. Specifically, the approach may be inserted as a useful routine in data mining area for improving the study of essential features supported by empirical evidence.

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Addictive

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