Business Rule Learning with Interactive Selection of Association Rules
نویسندگان
چکیده
This paper presents the implementation of a classification system based on learning of association rules in conjunction with Drools rule engine. The rules are interactively discovered with a web-based data mining system EasyMiner.eu. The rules are approved and edited by the domain expert before they are deployed for classification.
منابع مشابه
Optimizing Membership Functions using Learning Automata for Fuzzy Association Rule Mining
The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. The quality of mining fuzzy association rules depends on membership functions and since t...
متن کاملTransforming Association Rules to Business Rules: EasyMiner meets Drools
EasyMiner (easyminer.eu) is a web-based association rule mining software based on the LISp-Miner system. This paper presents a proof-of-concept workflow for learning business rules with EasyMiner from transactional data. The approved rules are exported to the Drools business rules engine in the DRL format. The main focus is the transformation of GUHA association rules to DRL.
متن کاملPrioritization of association rules in data mining: Multiple criteria decision approach
Data mining techniques, extracting patterns from large databases are the processes that focus on the automatic exploration and analysis of large quantities of raw data in order to discover meaningful patterns and rules. In the process of applying the methods, most of the managers who are engaging the business encounter a multitude of rules resulted from the data mining technique. In view of mul...
متن کاملFeatures Selection and Rule Removal for Frequent Association Rule Based Classification
The performance of association rule based classification is notably deteriorated with the existence of irrelevant and redundant features and complex attributes. Association rules naturally often suffer from a large volume of rules generated, many of which are not interesting and useful. Thus, selecting relevant feature and/or removing unrelated rules can significantly improve the association ru...
متن کاملLearning Business Rules with Association Rule Classifiers
The main obstacles for a straightforward use of association rules as candidate business rules are the excessive number of rules discovered even on small datasets, and the fact that contradicting rules are generated. This paper shows that Association Rule Classification algorithms, such as CBA, solve both these problems, and provides a practical guide on using discovered rules in the Drools BRMS...
متن کامل