关联规则学习
Apriori算法
前因(行为心理学)
计算机科学
数据挖掘
背景(考古学)
先验与后验
算法
联想(心理学)
领域(数学分析)
机器学习
人工智能
数学
生物
认识论
数学分析
发展心理学
哲学
古生物学
心理学
作者
Salma Khan,Muhammad Shaheen
标识
DOI:10.1177/01655515221108695
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI