Investigation on user web navigation using enhanced active ontology cluster and subjective association rules

J. Vellingiri, S. Kaliraj, S. Satheeshkumar, T. Parthiban

Research output: Contribution to journalArticlepeer-review


In recent years, the problem of predicting a user's navigation behavior on a web-site has obtained a lot of research interest. Gathering of useful information from the web has become a challenging issue for users. Current web information gathering systems endeavor to gratify user necessities by capturing their information needs using the data mining techniques. For this purpose, user profiles are fashioned for user background knowledge depiction. Personalized Ontology Model is extensively used to represent user profiles in modified web information gathering. The text classification and clustering along with the association rule mining strategies are suggested as the further improvement. As to investigate the user web navigation using integration of clustering and association rule mining techniques, introduced a new technique named Enhanced Active Ontology Clustering model with Subjective Sustain Association Rule mining. Modeling user web navigation data mining technique is a challenging task that continues to gain importance as the size of the web access and its user usage increases. The Enhanced Active Ontology Clustering model (EAOC) efficiently clusters the classified user web navigation data. EAOC model integrates the Subjective Sustain Association Rule mining (SSAR) to investigate the quality of transactions. Experimental results show that EAOC model clusters the web navigation based on user profile, buying patterns, product identity and seasons. The SSAR technique investigates and providesl8 - 20 % quality result on user web navigation when compared to existing system. Performance evaluation show benefits in terms of cluster optimality in terms of MB, and execution time in seconds on UCI Anonymous Web Data Data Set, CLUTO Software for Clustering High-Dimensional Datasets and Internet Usage Data Data Set.

Original languageEnglish
Pages (from-to)3769-3788
Number of pages20
JournalInformation (Japan)
Issue number8
Publication statusPublished - 01-08-2014

All Science Journal Classification (ASJC) codes

  • Information Systems


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