Web Access Log Mining with Soft Sequential Patterns

Abstract : Mining the time-stamped numerical data contained in web access logs is interesting for numerous applications (e.g. customer targeting, automatic updating of commercial websites or web server dimensioning). In this context, the algorithms for sequential patterns mining do not allow processing numerical information frequently. In previous works we defined fuzzy sequential patterns to cope with the numerical representation problem. In this paper, we apply these algorithms to web mining and assess them through experiments showing the relevancy of this work in the context of web access log mining.
Document type :
Conference papers
Complete list of metadatas

Cited literature [14 references]  Display  Hide  Download

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00095914
Contributor : Martine Peridier <>
Submitted on : Monday, October 7, 2019 - 7:53:10 PM
Last modification on : Tuesday, October 8, 2019 - 3:08:44 PM

Identifiers

Collections

Citation

Céline Fiot, Anne Laurent, Maguelonne Teisseire. Web Access Log Mining with Soft Sequential Patterns. Applied Artificial Intelligence, Aug 2006, Genova, Italy. pp.519-524, ⟨10.1142/9789812774118_0074⟩. ⟨lirmm-00095914⟩

Share

Metrics

Record views

106

Files downloads

12