Efficient algorithm for mining weighted sequential patterns based on graph traversals

To solve weighted traversal patterns mining problem, this paper generalizes the classes of weighted directed graph (WDG) and proposes a transformational model between edge-weightecl directed graph (EWDG) and vertex-weighted dirlcted graph (VWDG). Based on the model, an effective algorithm called GTWSPMiner, is devised to discover weighted traversal patterns from weighted traversals database of the WDG. Based on the property that the items in a traversal pattern are consecutive, the algorithm adopts a weighted prefix-projected sequence pattern growth approach to decompose the task of mining original sequence database into a series of smaller tasks of mining locally projected database. Contrastive experimental results show that the algorithm is competent to mine weighted frequent traversal patterns efficiently.