User Behaviour Pattern Mining from Weblog

摘要

In this paper, the authors build a tree using both frequent as well as non-frequent items and named as Revised PLWAP with Non-frequent Items RePLNI-tree in single scan. While mining sequential patterns, the links related to the non-frequent items are virtually discarded. Hence, it is not required to delete or maintain the information of nodes while revising the tree for mining updated weblog. It is not required to reconstruct the tree from scratch and re-compute the patterns each time, while weblog is updated or minimum support changed, since the algorithm supports both incremental and interactive mining. The performance of the proposed tree is better, even the size of incremental database is more than 50% of existing one, while it is not so in recently proposed algorithm. For evaluation purpose, the authors have used the benchmark weblog and found that the performance of proposed tree is encouraging compared to some of the recently proposed approaches.