Abstract :
Sequential pattern mining is of great importance in many applications including computational biology study, consumer behavior analysis, system performance analysis, etc. Recently, an extension of sequential patterns, called time-interval patterns, is proposed by Chen, Jiang, and Ko, which not only reveals the order of items but also the time intervals between successive items. Based on the time-interval patterns proposed by Chen, in this paper, we present a more efficient algorithm which uses the oriented graph to discover the time-interval patterns. The experimental results show that our algorithm is faster than I-Apriori.
Keywords :
data mining; graph theory; pattern recognition; oriented graph; successive items; time-interval sequential patterns mining; Application software; Biology computing; Business; Computational biology; Computer science; Databases; Pattern analysis; Performance analysis; Sequences; Software engineering; Data mining; sequential patterns; time interval pattern;
