Granular association rules with four subtypes

Relational data mining approaches look for patterns that involve multiple tables; therefore they become popular in recent years. In this paper, we introduce granular association rules to reveal connections between concepts in two universes. An example of such an association might be "men like alcohol." We present four meaningful explanations corresponding to four subtypes of granular association rules. We also define five measures to evaluate the quality of rules. Based on these measures, the relationships among different subtypes are revealed. This work opens a new research trend concerning granular computing and associate rule mining.

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