Data Mining Algorithm of Browsing Pattern Based on Web Log

An Web log contains a large number of user browsing information, so how to effectively mine it for user browsing pattern is an important research subject. Based on the analysis of the problems in the current mining algorithm of the user browsing pattern, and combining the characteristics of the existing fast association rules mining algorithm, this paper adds the sequential constraint and the time factor, and puts forward a browsing pattern mining algorithm TBPM which is based on the temporal constraint. It also designs incremental updating algorithm based on the temporal frequent item set algorithm TBPM. At last, it makes a comparison with the related work of the class Apriori algorithm, and the experimental results on the actual data have verified the effectiveness of this algorithm.