PrefixUnion: Mining traversal patterns efficiently in virtual environments

Abstract

Sequential pattern mining is an important data mining problem with broad applications. Especially, it is also an interesting problem in virtual environments. In this paper, we propose a projection-based, sequential pattern-growth approach, called PrefixUnion. Meanwhile, we also introduce the relationships among transactions, views and objects. According to these relationships, we suggest two mining criteria - inter-pattern growth and intra-pattern growth, which utilize these characteristics to offer ordered growth and reduced projected database. As a result, the large-scale VRML models could be accessed more efficiently, allowing for a real-time walk-through in the scene. © Springer-Verlag Berlin Heidelberg 2005.