AKAMAS: Mining association rules using a new algorithm for the assessment of the risk of coronary heart events

Many algorithms have been developed for rule mining in large transaction databases. Discovery of some important association rules is a main database mining problem. The objective of this study was to develop a new data mining algorithm named AKAMAS using different measures to extract the most important association rules for the assessment of heart event related risk factors. The implemented measures were: support, confidence, p-value, chi square, coverage, prevalence, recall, specificity, accuracy, lift, leverage, added value, relative risk, odds ratio, and conviction. The AKAMAS algorithm is a variant of the Apriori algorithm, the main difference is that it does not use the iterative technique of k-itemset to build the (k +1)- itemsets. It needs only one pass for extracting frequent itemsets. Although AKAMAS gave similar rules to Apriori it offers a wide selection of measures for filtering the best rules, including the computation of the chi square test, and its associated probability value (i.e. if a rule is statistically significant or not). Moreover, the rules are more comprehensively represented and are more easily to interpret.