Title: Softening the blow of frequent sequence analysis: soft constraints and temporal accuracy
Authors: Celine Fiot, Anne Laurent, Maguelonne Teisseire
Addresses: LIRMM, 161 rue Ada, 34080 Montpellier, France. ' LIRMM, 161 rue Ada, 34080 Montpellier, France. ' LIRMM, 161 rue Ada, 34080 Montpellier, France
Abstract: 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 Generalised Sequential Patterns (GSPs). 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 GSPs. We also propose a temporal accuracy measure to provide the user with a tool for analysing the numerous extracted patterns.
Keywords: data mining; generalised sequential patterns; time constraints; fuzzy set theory; temporal accuracy; web mining; frequent sequence analysis; temporal knowledge; sequence graphs; soft temporal constraints; fuzzy logic.
International Journal of Web Engineering and Technology, 2009 Vol.5 No.1, pp.24 - 47
Published online: 06 May 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article