Softening the Blow of Frequent Sequence Analysis: Soft Constraints and Temporal Accuracy

Céline Fiot (1) , Anne Laurent (2) , Maguelonne Teisseire (2)

1 LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 TATOO - Fouille de données environnementales

Mining temporal knowledge has many applications. Such knowledge can be all the more interesting as some time constraints between events can be integrated during the mining task. Both in data mining and machine learning, some methods have been proposed to extract and manage such knowledge using temporal constraints. In particular, some work has been done to mine generalized sequential patterns. However, such constraints are often too crisp or need a very precise assessment to avoid erroneous information. Within this context, we propose an approach based on sequence graphs derived from soft temporal constraints. These relaxed constraints enable us to find more generalized sequential patterns. We also propose a temporal accuracy measure to provide the user with a tool for analysing the numerous extracted patterns.

Mots clés: Data mining; Sequential patterns; Time constraints; Fuzzy set theory; Temporal accuracy; Web mining

Domaines: Base de données [cs.DB]

Céline Fiot, Anne Laurent, Maguelonne Teisseire. Softening the Blow of Frequent Sequence Analysis: Soft Constraints and Temporal Accuracy. International Journal of Web Engineering and Technology, 2009, Web-based Knowledge Representation and Management, pp.24-47. ⟨10.1504/IJWET.2009.025012⟩. ⟨lirmm-00196964⟩