Mining Multirelation Association Rules in Graphs: Driving the Search Process

• Felipe A. de Oliveira Military Institute of Engineering (IME)
• Raquel L. Costa National Cancer Institute (INCA)
• Ronaldo R. Goldschmidt Military Institute of Engineering (IME)
• Maria C. Cavalcanti Military Institute of Engineering (IME) https://orcid.org/0000-0003-4965-9941

DOI: https://doi.org/10.5753/sbbd.2017.174664

Abstract

Nowadays, the Web of Data is a highly diverse and rich source of information. One of its great challenges lies in extracting useful information that leads to knowledge and advancement in the scientific area. The data mining algorithms help in the process of knowledge discovery, based on different search strategies. However, in general, they produce a considerable amount of rules, which are difficult to manipulate by the user. In this work, we present an adaptation of the MRAR algorithm based on the search mask concept, which is used to direct the mining process in order to find rules that can really be useful to the user in a shorter time.

Keywords: Data Mining, MRAR, Search Mask

References

Agrawal, R., Imieliński, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. SIGMOD Rec., 22(2):207–216.

Elseidy, M., Abdelhamid, E., and Skiadopoulos, S. (2014). GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph. Proceedings of the VLDB Endowment, 7(7):517–528.

Goldschmidt, R., Bezerra, E., and Passos, E. (2015). Data mining: conceitos, técnicas, algoritmos, orientações e aplicações. Rio de Janeiro: Elsevier.

Hendrickx, T., Cule, B., Meysman, P., Naulaerts, S., Laukens, K., and Goethals, B. (2015). Mining Association Rules in Graphs Based on Frequent Cohesive Itemsets, pages 637–648. Springer International Publishing, Cham.

Ramezani, R. (2014). MRAR : Mining Multi-Relation Association Rules. Journal of Computing and Security, 1(2):133–158.