U3 - mining unordered embedded subtrees using TMG candidate generation

Abstract

In this paper we present an algorithm for mining of unordered embedded subtrees. This is an importantproblem for association rule mining from semistructured documents, and it has important applications in many biomedical, web and scientific domains. The proposed U3 algorithm is an extension of our general tree model guided (TMG) candidate generation framework and it considers both transaction based and occurrence match support. Synthetic and real world data sets are used to experimentally demonstrate the efficiency of our approach to the problem, and the flexibility of our general TMG framework.