医疗健康数据的模糊粗糙集规则挖掘方法研究

医疗健康数据通常属性较多,且存在连续型、离散型并存的混合数据,这在很大程度上限制了知识发现方法对医疗健康数据的挖掘效率。以模糊粗糙集理论为基础,研究混合数据上的分类规则挖掘方法,通过引入规则获取算法的泛化阈值,来控制获取规则集的大小和复杂程度,提高粗糙集知识发现方法在医疗健康数据上的分类效率。最后通过对比实验验证了该算法在医疗决策表上挖掘规则的有效性。

Research on Fuzzy Rough Sets Based Rule Induction Methods for Healthcare Data

Healthcare databases typically contain numerous attributes,and have both continuous and discrete type of attributes in hybrid data,which limits the mining efficiency of knowledge discovery on healthcare data to a great extent.Based on fuzzy rough sets theory,we studied the classification rule mining methods on hybrid data.By introducing the generalization thresholds for rule induction algorithm,the proposed method can reduce the size of extracted rule set and complexity of rules,which may improve the classification efficiency of rough sets based knowledge discovery method on healthcare data.Finally we conducted comparative experiments on medical decision tables to verify the effectiveness of mined rules of proposed algorithm.