Gradual Trends in Fuzzy Sequential Patterns

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

1 AxIS - Usage-centered design, analysis and improvement of information systems
2 TATOO - Fouille de données environnementales

Fuzzy sequential pattern mining is a relevant approach when dealing with temporally annotated numerical data since it allows discovering frequent sequences embedded in the records. However, such patterns, in their current form, do not allow extracting another kind of knowledge that is typical of sequential data: temporal tendencies. Thanks to a relevant use of fuzzy sequential patterns, we propose the GraSP algorithm that discovers gradual trends in sequences. Our proposal is validated through experiments on web access logs.

Mots clés: Sequential Patterns; Data Mining; Sequence Database; Trends; Gradual Rules

Domaines: Base de données [cs.DB]

Céline Fiot, Florent Masseglia, Anne Laurent, Maguelonne Teisseire. Gradual Trends in Fuzzy Sequential Patterns. IPMU: Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 2008, Malaga, Spain. pp.456-463. ⟨lirmm-00273910⟩