Mining up-to-date knowledge from log data
In this paper, a new concept of up-to-date patterns is proposed, which is a hybrid of the association rules and temporal mining. An up-to-date pattern is composed of an item set and its up-to-date lifetime, in which the user-defined minimum support threshold must be satisfied. The proposed approach can mine more useful large itemsets than the conventional ones which discover large itemsets valid only for the entire database. Experimental results also show the performance of the proposed approach.