Research Article Open Access

A User-Driven Association Rule Mining Based on Templates for Multi-Relational Data

Carlos Roberto Valêncio1, Guilherme Henrique Morais1, Márcio Zamboti Fortes2, Angelo Cesar Colombini2, Leandro Alves Neves1, Mario Luiz Tronco3 and William Tenório1
  • 1 São Paulo State University (Unesp), Brazil
  • 2 Fluminense Federal University (UFF), Brazil
  • 3 São Paulo University (EESC-USP), Brazil


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.

Journal of Computer Science
Volume 14 No. 11, 2018, 1475-1487


Submitted On: 10 April 2018 Published On: 8 November 2018

How to Cite: Valêncio, C. R., Morais, G. H., Fortes, M. Z., Colombini, A. C., Neves, L. A., Tronco, M. L. & Tenório, W. (2018). A User-Driven Association Rule Mining Based on Templates for Multi-Relational Data. Journal of Computer Science, 14(11), 1475-1487.

  • 1 Citations



  • Data Mining
  • Templates
  • Association Rules
  • Knowledge Discovery in Databases
  • Multi-relational Data Mining
  • User-Driven Filter