CLUIN – A new method for extracting rules for large databases

Abstract

When there is a need to understand the data stored in a database, one of the main requirements is being able to extract knowledge in the form of rules. Classification strategies allow extracting rules almost naturally. In this paper, the CLUHR classification strategy is extended to work with databases that have nominal attributes. Finally, the results obtained using the databases from the UCI repository are presented and compared with other existing classification models, showing that the algorithm presented requires less computational resources and achieves the same accuracy level and number of extracted rules.