A User-Driven Association Rule Mining Based on Templates for Multi-Relational Data
Carlos Roberto Valêncio, Guilherme Henrique Morais, Márcio Zamboti Fortes, Angelo Cesar Colombini, Leandro Alves Neves, Mario Luiz Tronco and William Tenório
Journal of Computer Science
Data mining algorithms to find association rules are an important tool to extract knowledge from databases. However, these algorithms produce an enormous amount of rules, many of which could be redundant or irrelevant for a specific decision-making process. Also, the use of previous knowledge and hypothesis are not considered by these algorithms. On the other hand, most existing data mining approaches look for patterns in a single data table, ignoring the relations presented in relational databases. The contribution of this paper is the proposition of a multi-relational data mining algorithm based on association rules, called TBMR-Radix, which considers previous knowledge and hypothesis through the using of the Templates technique. Applying this approach over two real databases, we were able to reduce the number of generated rules, use the existing knowledge about the data and reduce the waste of computational resources while processing. Our experiments show that the developed algorithm was also able to perform in a multi-relational environment, while the MR-Radix, that does not use Templates technique, was not.
© 2018 Carlos Roberto Valêncio, Guilherme Henrique Morais, Márcio Zamboti Fortes, Angelo Cesar Colombini, Leandro Alves Neves, Mario Luiz Tronco and William Tenório. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.