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Upgrading a Granular Computing Based Data Mining Framework to a Relational Case
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DOI：10.1002/int.21644.png)
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One of the popular methods to develop an algorithm for mining data stored in a relational structure is to upgrade an existing attribute-value algorithm to a relational case. Current approaches to this problem have some shortcomings such as (1) a dependence on the upgrading process of the algorithm to be extended, (2) complicated redefinitions of crucial notions (e.g., pattern generality, pattern refinement), and (3) a tolerant limitation of the search space for pattern discovery. In this paper, we propose and evaluate a general methodology for upgrading a data mining framework to a relational case. This methodology is defined in a granular computing environment. Thanks to our relational extension of a granular computing based data mining framework, the three above problems can be overcome.
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APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
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