title: Effect of temporal relationships in associative rule mining for web log data.
abstract: The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality.
authors.affiliation: Faculty of Computer Sciences and Information Technology, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia.
authors.forename: Nazli Mohd
authors.initials: NM
authors.lastname: Khairudin
authors.affiliation: Faculty of Computer Sciences and Information Technology, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia.
authors.forename: Aida
authors.initials: A
authors.lastname: Mustapha
authors.affiliation: Faculty of Computer Sciences and Information Technology, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia.
authors.forename: Mohd Hanif
authors.identifier: 0000-0001-7118-6728
authors.initials: MH
authors.lastname: Ahmad
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D020407:Internet; D011996:Records; D013997:Time Factors