Mining negative rules: a literature review focusing on performance
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Data
2021-01-01
Autores
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.
Título da Revista
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Título de Volume
Editor
Ieee
Resumo
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.
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Palavras-chave
Data mining, Frequent patterns, Negative association rules, Parallel algorithms, Systematic literature review
Como citar
Proceedings Of 2021 16th Iberian Conference On Information Systems And Technologies (cisti'2021). New York: Ieee, 6 p., 2021.