Modification of A priori Algorithm focused on confidence value to association rules

Abstract

A priori algorithm is one of the data mining algorithm in formation of rule mining association. A priori algorithm is the process of extraction of information from a database, followed by frequent item / itemset and candidate generation in formation of association rule mining in order to obtain minimum value of support and minimum confidence value. The value of confidence has a big effect on rule the resulting, where the rule generated by the k-item sets pattern needs to be calculated on the level of confidence or certainty of the k-item sets pattern that has complied with the rules. Therefore, this research discusses about a priori algorithm modification which focuses on giving confidence value for each rule generated. Modifications are made by substituting the Bayesian method on a standard A priori confidence formula. And for the next process there is difference of confidence value between a priori standard and modification, where the value of confidence generated a priori modification is bigger, and after calculated for some rules taken according to minimum support condition then there is average difference of confidence value equal to 10,50%.