Mining negative rules: a literature review focusing on performance - Colombo, Alexandre [UNESP]; Spolon, Roberta [UNESP]; Lobato, Renata Spolon [UNESP]; Manacero Junior, Aleardo [UNESP]; Cavenaghi, Marcos Antonio; Rocha, A.; Goncalves, R.; Penalvo, F. G.; Martins, J. (Ieee, 2021)
Mining of frequent patterns and association rules is a Data Mining task that aims to determine consistent relationships among elements in a transaction database. Algorithms that consider the absence of elements perform the generation of so-called negative rules which result in associations of great interest for some applications, enabling it to obtain extra knowledge in comparison to the positive case. This type of association presents a problem regarding the increased amount of generated rules which demands adequate computational resources. This study presents a systematic review with the aim of grouping the concepts of the main contemporary works on this topic, in order to assist the development of future works in this subject.
Keywords: Data mining; Frequent patterns; Negative association rules; Parallel algorithms; Systematic literature review