Abstract
This chapter develops a general granular computing based framework for mining relational data. The framework provides a granular representation of relational data that is constructed based on target objects and the sets of background objects related to the target ones. The chapter also shows the possibility of deriving relational patterns from the granular representation.
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Notes
- 1.
The notions of relation and table are used in this monograph interchangeably.
- 2.
The standard information system is understood as the Pawlak information system.
- 3.
\(A_R\) denotes here the set of all attributes of relation R.
- 4.
It is assumed that the value of an attribute is specified for a given object if and only if the object belongs to the relation whose schema includes the attribute.
- 5.
The tables the objects belong to are not assumed to be different.
- 6.
A component of an object can be replaced with either a variable, a set of constants, or symbol “\(\_\)” if the component is not important for the consideration.
- 7.
The denotation \(\{v_1,v_2,\dots ,v_n\}\) that occurs in an object argument list means that the corresponding attribute may take any of the values \(v_1,v_2,\dots ,v_n\). We assume that sets are formed for attributes that take on a relatively small number of values. Otherwise, the attributes are previously discretized.
- 8.
One of relations \(R_i\) is usually considered as the target one. However, such a relation, as in this approach, may be determined externally, i.e. it occurs in the database but not in the pattern.
- 9.
One can also consider rules including negated descriptors or conditions formed based on arguments of descriptors previously added.
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© 2017 Springer International Publishing AG
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Hońko, P. (2017). Information System for Relational Data. In: Granular-Relational Data Mining. Studies in Computational Intelligence, vol 702. Springer, Cham. https://doi.org/10.1007/978-3-319-52751-2_2
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DOI: https://doi.org/10.1007/978-3-319-52751-2_2
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Online ISBN: 978-3-319-52751-2
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