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Conference Papers Year : 2016

Automatic Formation of Sets of Contrasting Rules to Identify Trigger Factors

Abstract

Pattern identification in datasets has been the focus of many research works. Data mining, through association rules mining, is one of the best known approaches. However, it results most of the time in a huge set of patterns (rules), so their exploitation is not easy and often requires experts analysis. In this paper, we introduce a new pattern, referred to as “set of contrasting rules”. Contrary to most of the patterns from the state-of- the-art, this pattern has the characteristic of being made up of a set of rules. It has also the advantage of not only identifying a reduced set of rules, but also structuring it into sets. One main originality of this pattern is that it allows to easily identify trigger factors: factors that can bring some event state changes. In real applications, this pattern can thus be used to influence the values of some attributes. The experiments conducted on a real dataset of census data, con- firm that the patterns discovered are made up of a reduced set of rules and that they can actually be used to influence some attributes in this dataset.
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Dates and versions

hal-01336303 , version 1 (22-06-2016)

Identifiers

  • HAL Id : hal-01336303 , version 1

Cite

Marharyta Aleksandrova, Armelle Brun, Oleg Chertov, Anne Boyer. Automatic Formation of Sets of Contrasting Rules to Identify Trigger Factors. ECAI - European Conference on Artificial Intelligence, Aug 2016, La Hague, Netherlands. ⟨hal-01336303⟩
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