Mining Frequent Patterns from Hypergraph Databases

Abstract

Hypergraph is a complex data structure capable of expressing associations among any number of data entities. Overcoming the limitations of traditional graphs, hypergraphs are useful to model real-life problems. Frequent pattern mining is one of the most popular problems in data mining with a lot of applications. To the best of our knowledge, there exists no flexible frequent pattern mining framework for hypergraph databases decomposing associations among data entities. In this work, we propose a flexible and complete framework for mining frequent patterns from a collection of hypergraphs. We also develop an algorithm for mining frequent subhypergraphs by introducing a canonical labeling technique for isomorphic subhypergraphs. Experiments conducted on real-life hypergraph databases demonstrate both the efficiency of the algorithm and the effectiveness of the proposed framework.