维持
发表于 2025-3-23 10:05:53
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失望未来
发表于 2025-3-23 14:53:20
https://doi.org/10.1007/978-3-662-01075-4tools were based on either univariate (bivariate) data representations or on “slick” transformations of multivariate information perceivable by the human eye. Most of the tools are extremely useful in a modelling step, but unfortunately, do not give the full picture of the data set. One reason for t
收集
发表于 2025-3-23 20:51:18
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graphy
发表于 2025-3-24 00:52:16
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airborne
发表于 2025-3-24 03:15:37
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妈妈不开心
发表于 2025-3-24 09:05:12
https://doi.org/10.1007/978-3-658-40755-1variate or univariate devices used to reduce the dimensions of the observations. In the following three chapters, issues of reducing the dimension of a multivariate data set will be discussed. The perspectives will be different but the tools will be related.
epinephrine
发表于 2025-3-24 13:49:21
https://doi.org/10.1007/978-3-658-40755-1 Principal components analysis has the same objective with the exception that the rows of the data matrix . will now be considered as observations from a .-variate random variable .. The principle idea of reducing the dimension of . is achieved through linear combinations. Low dimensional linear com
Isolate
发表于 2025-3-24 18:28:40
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LASH
发表于 2025-3-24 20:43:11
https://doi.org/10.1007/978-3-658-41831-1situations can arise. Given a data set containing measurements on individuals, in some cases we want to see if some natural groups or classes of individuals exist, and in other cases, we want to classify the individuals according to a set of existing groups. Cluster analysis develops tools and metho
喧闹
发表于 2025-3-24 23:34:53
https://doi.org/10.1007/978-3-031-36043-5 observations, into these known groups. For instance, in credit scoring, a bank knows from past experience that there are good customers (who repay their loan without any problems) and bad customers (who showed difficulties in repaying their loan). When a new customer asks for a loan, the bank has t