A survey of uncertainty handling in frequent subgraph mining algorithms

Frequent subgraph mining is useful in most knowledge discovery tasks such as classification, clustering and indexing. Many algorithms and methods have been developed to mine frequent subgraphs. To have an understanding of several mining frequent subgraph algorithms, it is advantageous to establish a common framework for their study. In this paper, we propose a comparative study of several approaches by focusing on the intrinsic characteristics of these algorithms. A set of existing approaches in literature are reviewed and categorized according to the certainty nature of input which can be exact or uncertain graphs.