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A second group of modifications concerns the relational associations between tags and questions, and between related questions to facilitate the user's navigation interface to explore questions.
This strategy for RDF n-ary representation of relational associations between two or more entities has been widely adopted.
Somewhat akin to driving (albeit less complex), temporal discounting tasks require processing relational associations and estimating reward size and magnitude, cost benefit ratios, and timing.
The nevertheless has many of the characteristics of a formal ontology: machine-readability, formal notation, a hierarchical knowledge structure, and relational associations between concepts.
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We propose a novel classification model based on relational association rules mining.
Our classifier is based on the discovery of relational association rules for predicting whether a software module is or it is not defective.
Relational association rules are an extension of ordinal association rules, which are a particular type of association rules that describe numerical orderings between attributes that commonly occur over a dataset.
Clinical variables were tested for associations with mutational results for all genes analyzed, and all genes were tested for inter-relational associations.
The extraction of multi-relational association rules can also be done with graphs.
The MR-Radix algorithm proposed in this work is based on the PatriciaMine algorithm which can extract multi-relational association rules from large relational databases.
Another strategy for the extraction of multi-relational association rules is based on traditional algorithms, such as Apriori and FP-growth.
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