An effective mining approach for up-to-date patterns

Mining association rules is most commonly seen among the techniques for knowledge discovery from databases (KDD). It is used to discover relationships among items or itemsets. Furthermore, temporal data mining is concerned with the analysis of temporal data and the discovery of temporal patterns and regularities. 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 itemset may not be frequent (large) for an entire database but may be large up-to-date since the items seldom occurring early may often occur lately. An up-to-date pattern is thus composed of an itemset 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 show that the proposed algorithm is more effective than the traditional ones in discovering such up-to-date temporal patterns especially when the minimum-support threshold is high.