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Titlebook: Understanding High-Dimensional Spaces; David B. Skillicorn Book 2012 The Author 2012 Clusters.Context.Counterintelligence.Data mining.Data

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樓主: HEMI
21#
發(fā)表于 2025-3-25 03:22:58 | 只看該作者
22#
發(fā)表于 2025-3-25 09:48:52 | 只看該作者
23#
發(fā)表于 2025-3-25 13:38:07 | 只看該作者
24#
發(fā)表于 2025-3-25 17:02:49 | 只看該作者
Spaces with Multiple Centers, is one underlying process responsible for generating the data, and that the spatial variation around some notional center is caused by some variation overlaying this process. Often, perhaps most of the time, it is much more plausible that there are multiple, interacting processes generating the dat
25#
發(fā)表于 2025-3-25 21:15:14 | 只看該作者
Using Models of High-Dimensional Spaces,leton for such a space. We have also seen how to divide up a space into qualitative regions that allow outliers and small clusters to be assessed and interpreted in terms of what their impact on existing models should be.
26#
發(fā)表于 2025-3-26 04:00:49 | 只看該作者
Including Contextual Information,ion, and only once for each particular dataset. Nothing could be further from the truth. The process of exploring and understanding a dataset is always iterative, and the results of each round, and the deeper understanding that comes from it, inform the strategy and tactics of the next round.
27#
發(fā)表于 2025-3-26 05:14:47 | 只看該作者
28#
發(fā)表于 2025-3-26 09:01:42 | 只看該作者
Book 2012ts attributes, with each record represented as a point in the space with its position depending on its attribute values. Such spaces are not easy to work with because of their high dimensionality: our intuition about space is not reliable, and measures such as distance do not provide as clear inform
29#
發(fā)表于 2025-3-26 14:28:29 | 只看該作者
2191-5768 dimensional spaces using two models.Valuable for practitione.High-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depe
30#
發(fā)表于 2025-3-26 19:02:53 | 只看該作者
Spaces with a Single Center,esembling each other less; and outside this are records that are much more scattered, much less frequent, and very untypical. The reason that this structure seems intuitively appealing is that, as records become inherently more unusual (further from the center), they also become less alike (because of the different directions).
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