OrderSpan: Mining Closed Partially Ordered Patterns

Mickaël Fabrègue (1, 2) , Agnès Braud (2) , Sandra Bringay (3, 4) , Florence Le Ber (2) , Maguelonne Teisseire (1, 3)

1 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
2 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
3 ADVANSE - ADVanced Analytics for data SciencE
4 UPVM - Université Paul-Valéry - Montpellier 3

Due to the complexity of the task, partially ordered pattern mining of sequential data has not been subject to much study, despite its usefulness. This paper investigates this data mining challenge by describing OrderSpan, a new algorithm that extracts such patterns from sequential databases and overcomes some of the drawbacks of existing methods. Our work consists in providing a simple and flexible framework to directly mine complex sequences of itemsets, by combining well-known properties on prefixes and suffixes. Experiments were performed on different real datasets to show the benefit of partially ordered patterns.

Domaines: Intelligence artificielle [cs.AI]

Mickaël Fabrègue, Agnès Braud, Sandra Bringay, Florence Le Ber, Maguelonne Teisseire. OrderSpan: Mining Closed Partially Ordered Patterns. IDA: Intelligent Data Analysis, Oct 2013, London, United Kingdom. pp.186-197, ⟨10.1007/978-3-642-41398-8_17⟩. ⟨hal-00880290⟩