Data Mining of User Navigation Patterns

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Data Mining of User Navigation Patterns (2000)

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by Jose Borges , Mark Levene

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151 - 19 self

BibTeX

@INPROCEEDINGS{Borges00datamining,
    author = {Jose Borges and Mark Levene},
    title = {Data Mining of User Navigation Patterns},
    booktitle = {},
    year = {2000},
    pages = {92--111},
    publisher = {Springer-Verlag}
}

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Abstract

We propose a data mining model that captures the user navigation behaviour patterns. The user navigation sessions are modelled as ahypertext probabilistic grammar whose higher probability strings correspond to the user's preferred trails. An algorithm to efficiently mine suchtrailsisgiven. Wemake use of the Ngram model which assumes that the last N pages browsed affect the probability of the next page to be visited. The model is based on the theory of probabilistic grammars providing it with a sound theoretical foundation for future enhancements. Moreover, we propose the use of entropy as an estimator of the grammar's statistical properties. Extensive experiments were conducted and the results show that the algorithm runs in linear time, the grammar's entropy is a good estimator of the number of mined trails and the real data rules confirm the effectiveness of the model.

Keyphrases

user navigation pattern data mining next page data mining model probability string correspond statistical property good estimator linear time last page ngram model real data rule future enhancement sound theoretical foundation probabilistic grammar wemake use user navigation session user navigation behaviour pattern extensive experiment ahypertext probabilistic grammar

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