Abstract
This chapter introduces a rough-granular computing approach defined for relational data. The core of this approach is the tolerance rough set model that is adapted to data stored in a relational databases. The chapter also defines a range of similarity measures for relational data. They are used in the construction of one of the tolerance rough set model components, i.e. the uncertainty function.
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Notes
- 1.
Symbols \(c_i,m_i,p_i,p^\prime _i\) denote the i-th object of tables \(customer,married\_to,purchase,product\), respectively.
- 2.
Here, attribute \(prod\_id\) is treated as nominal.
- 3.
The measure can be used for sets of positive numbers only.
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Hońko, P. (2017). Rough-Granular Computing. In: Granular-Relational Data Mining. Studies in Computational Intelligence, vol 702. Springer, Cham. https://doi.org/10.1007/978-3-319-52751-2_5
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DOI: https://doi.org/10.1007/978-3-319-52751-2_5
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52750-5
Online ISBN: 978-3-319-52751-2
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