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- W1979366912 abstract "Data analysis and data mining are concerned with unsupervised pattern finding and structure determination in data sets. The data sets themselves are explicitly linked as a form of representation to an observational or otherwise empirical domain of interest. Structure has long been understood as symmetry which can take many forms with respect to any transformation, including point, translational, rotational, and many others. Beginning with the role of number theory in expressing data, we show how we can naturally proceed to hierarchical structures. We show how this both encapsulates traditional paradigms in data analysis, and also opens up new perspectives towards issues that are on the order of the day, including data mining of massive, high dimensional, heterogeneous data sets. Linkages with other fields are also discussed including computational logic and symbolic dynamics. The structures in data surveyed here are based on hierarchy, represented as p-adic numbers or an ultrametric topology." @default.
- W1979366912 created "2016-06-24" @default.
- W1979366912 creator A5000885553 @default.
- W1979366912 date "2009-07-01" @default.
- W1979366912 modified "2023-09-23" @default.
- W1979366912 title "Symmetry in data mining and analysis: A unifying view based on hierarchy" @default.
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- W1979366912 doi "https://doi.org/10.1134/s0081543809020175" @default.
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