Exceptional Association Rule Set Mining from Oral Health Assessment Database
Exceptional Association Rule Set Mining from Oral Health Assessment Database
复制标题
从口腔健康评估数据库中挖掘特殊关联规则集
DOI：
10.1007/978-3-319-67792-7_42
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Toru Naito
中科院分区：
文献类型：
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作者：
Kaoru Shimada;Satoshi Noguchi;Michiko Makino;Toru Naito
提出了一种从不完整数据库中发现异常关联规则集的扩展方法。该方法直接计算规则评估的比值比。例外规则集定义为：每个项目集X、Y分别与C类有弱统计关系或无统计关系；然而，X和Y的连接与c有很强的关系，异常规则集具有解释X和Y连接的长规则的潜力。该方法应用于口腔健康评估数据库的规则挖掘。我们获得了有趣的例外规则集，结果表明该方法在医疗保健领域的有效性。
This paper proposes an extended method to discover exceptional association rule sets from incomplete databases. The proposed method calculates an odds ratio directly for the rule evaluation. The exceptional rule set is defined as each itemset X, Y has a weak or no statistical relation to class C, respectively; however, the join of X and Y has a strong relation to C. The exceptional rule set has potential to interpret long rules for the join of X and Y. The proposed method is applied to rule mining for oral health assessment databases. We obtained interesting exceptional rule sets and the results showed effectiveness of the method in the medical and health care fields.