Effect of Temporal Relationships in Associative Rule Mining for Web Log Data. (23rd January 2014)

• Record Type: Journal Article
• Authors: Mohd Khairudin, Nazli; Mustapha, Aida; Ahmad, Mohd Hanif
• Other Names: Sossa J. H. Academic Editor.; Sun B. Academic Editor.
• Is Part Of: TheScientificWorldjournal. Volume 2014(2014)
• Publication Date: 2014-01-23
• Subjects: Science -- Periodicals; Technology -- Periodicals; Medicine -- Periodicals; 505
• DOI: 10.1155/2014/813983
• Languages: English
• ISSNs: 2356-6140

Abstract

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.