Mining frequent web access patterns with partial enumeration

  • Authors:
  • Peiyi Tang;Markus P. Turkia

  • Affiliations:
  • University of Arkansas at Little Rock, AR;University of Arkansas at Little Rock, AR

  • Venue:
  • ACM-SE 45 Proceedings of the 45th annual southeast regional conference
  • Year:
  • 2007

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Abstract

In this paper, we extend the pattern-growth web access pattern mining algorithms [1, 2, 3] with partial enumeration. The extended algorithm can grow the frequent patterns with more than one symbol at a time and unifies the pattern-growth and apriori algorithms [4]. The experimental results show that for the databases of long sequences, the best performance is neither given by the pattern-growth algorithms nor by the full apriori enumeration algorithms, but rather by the mining with partial enumeration in the middle.