Association rule discovery with fuzzy decreasing support on Syndrome Differentiation and medication in coronary heart disease

Association rules mining is an important research field in data mining. Over the years, a variety of algorithms to find frequent itemsets in very large transaction databases has been developed. The key feature in most of these algorithms is that they use a constant support constraint to control the inherently exponential complexity of the problem. Long itemsets with low support can still be interesting but it is unable to find them. In this paper, we present a new association rule mining framework: fuzzy decreasing support-confidence that finds all itemsets that satisfy a length-decreasing support constraint. On this basis, by analyzing the correlation between the antecedent and the consequent of the generated rules, we further propose two correction frameworks: (1) Fuzzy Decreasing Support, Confidence, Interestingness; (2) Fuzzy Decreasing Support, Bidirectional Confidence, Interestingness; We extract data about the relevant factors of Syndrome Differentiation and the patients´ medication from the coronary heart disease data collected from the hospitals. The experimental results show that the frameworks proposed in this paper not only verify the existing Syndrome Differentiation and regular patterns of medication, but also discover Syndrome Differentiation with a combination of factors and medicine compatibilities among multiple drugs.