Mining web navigation patterns with dynamic thresholds for navigation prediction

Jia Ching Ying*, Chu Yu Chin, Vincent Shin-Mu Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Discovering web navigation patterns is an important issue in web usage mining with various applications like navigation prediction and improvement of website management. Since web site structure is always changed, we need not only consider the frequency of click behavior but also web site structure to mine web navigation patterns for navigation prediction. To reduce the overhead of dynamically mining the web navigation patterns from the web data, a dynamic mining approach is needed by using the previous mining results and computing new patterns just from the inserted or deleted part of the web data. In this paper, we propose a special data structure named Ideal-Tree (Inverted-data-base Expectable Tree) to avoid the effort of scanning database. Meanwhile, an efficient mining algorithm named Ideal-Tree-Miner is proposed for mining web navigation patterns with dynamic thresholds. Based on the discovered patterns, we also give a navigation prediction model. The experimental results show that our prediction model outperforms other approaches substantially in terms of Precision, Recall, and F-measure.

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012
Pages614-619
Number of pages6
DOIs
StatePublished - 1 Dec 2012
Event2012 IEEE International Conference on Granular Computing, GrC 2012 - HangZhou, China
Duration: 11 Aug 201213 Aug 2012

Publication series

NameProceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012

Conference

Conference2012 IEEE International Conference on Granular Computing, GrC 2012
CountryChina
CityHangZhou
Period11/08/1213/08/12

Keywords

  • Incremental mining
  • Navigation prediction
  • Web mining
  • Web navigation pattern

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