WisRule: First cognitive algorithm of wise association rule mining

Abstract

This article proposes a new algorithm for a newly emerging domain wisdom mining that claims to extract wisdom from data. Association rule mining is one of the dominant data mining techniques based on which a new algorithm called WisRule is proposed that generates both positive and negative association rules. These rules can be used for decision-making with less influence from a specialist. The existing algorithms of association rule extraction are based on the frequency of an itemset, which was introduced into the Apriori algorithm for the first time. In these algorithms, those itemsets are converted to the rules of the form Antecedent ⇒ Consequent that qualify the threshold of support, confidence and similar other measures. WisRule is proposed as an extension to the CBPNARM algorithm. WisRule produces both positive and negative association rules based on their frequency evaluated in a certain context (C), utility (U), time (T) and location (L). Rules that are valid in a given context, have high utility and are valid across multiple time intervals and locations become part of the final ruleset. The evaluation of a rule in these four dimensions is claimed as mining wisdom from the given data that is currently used as a hypothetical basis for a domain expert’s decision. WisRule is compared with the Apriori, PNARM and CBPNARM algorithms in terms of precision, recall, number of rules, average confidence, F-measure and execution time.