Web usage mining with evolutionary extraction of temporal fuzzy association rules

Stephen G. Matthews*, Mario A. Gongora, Adrian A. Hopgood, Samad Ahmadi

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets' boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple linguistic representation to discover rules that occur at the intersection of fuzzy set boundaries. The GA-based approach is enhanced from previous work by including a graph representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algorithms, because it discovers extra rules.

    Original languageEnglish
    Pages (from-to)66-72
    Number of pages7
    JournalKnowledge-Based Systems
    Publication statusPublished - 2013


    • Fuzzy association rules
    • Temporal association rules
    • Evolutionary fuzzy system
    • Genetic algorithm
    • Data mining
    • Analytics
    • Rule discovery
    • 2-tuple linguistic representation


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