TABLE 3.

Application of Apriori algorithm in Chinese medicine data analysis in descending order of lift.

Antecedents	Consequents	Antecedent support	Consequent support	Support	Confidence	Lift	Leverage	Conviction
['當歸', '醋', '甘草']	['枳殻', '蜜']	0.886	0.821	0.804	0.907	1.105	0.076	1.929
['枳殻', '蜜']	['當歸', '醋', '甘草']	0.821	0.886	0.804	0.979	1.105	0.076	5.511
['當歸', '蜜', '醋', '甘草']	['枳殻']	0.884	0.823	0.804	0.910	1.105	0.076	1.955
['枳殻']	['當歸', '蜜', '醋', '甘草']	0.823	0.884	0.804	0.976	1.105	0.076	4.939
['大黄']	['黄芩']	0.852	0.852	0.801	0.940	1.103	0.075	2.476
['黄芩']	['大黄']	0.852	0.852	0.801	0.940	1.103	0.075	2.476
['當歸', '醋']	['枳殻', '蜜']	0.891	0.821	0.806	0.905	1.102	0.075	1.884
['枳殻', '蜜']	['當歸', '醋']	0.821	0.891	0.806	0.982	1.102	0.075	6.156
['枳殻', '蜜', '甘草']	['當歸', '醋']	0.818	0.891	0.804	0.982	1.102	0.075	6.138
['當歸', '醋']	['枳殻', '蜜', '甘草']	0.891	0.818	0.804	0.902	1.102	0.075	1.856
['枳殻']	['當歸', '蜜', '醋']	0.823	0.889	0.806	0.979	1.102	0.075	5.410
['當歸', '蜜', '醋']	['枳殻']	0.889	0.823	0.806	0.907	1.102	0.075	1.908
['枳殻', '甘草']	['當歸', '蜜', '醋']	0.821	0.889	0.804	0.979	1.102	0.074	5.394
['當歸', '蜜', '醋']	['枳殻', '甘草']	0.889	0.821	0.804	0.905	1.102	0.074	1.879
['當歸', '醋', '甘草']	['枳殻']	0.886	0.823	0.804	0.907	1.102	0.074	1.903
['枳殻']	['當歸', '醋', '甘草']	0.823	0.886	0.804	0.976	1.102	0.074	4.837
['木香', '當歸']	['白术', '蜜', '醋']	0.872	0.835	0.801	0.919	1.101	0.073	2.044
['白术', '蜜', '醋']	['木香', '當歸']	0.835	0.872	0.801	0.959	1.101	0.073	3.162
['白术', '醋']	['木香', '當歸', '蜜']	0.840	0.867	0.801	0.954	1.100	0.073	2.888
['木香', '當歸', '蜜']	['白术', '醋']	0.867	0.840	0.801	0.925	1.100	0.073	2.119