Discovery of Internal and External Hyperclique Patterns in Complex Graph Databases

In some applications, the whole structure of the target data can be represented naturally in "multi-structured graphs" that are complex graphs whose vertices consist of aset of structured data such as itemsets, sequences and so on. To catch the strong affinity relationship in multi-structured graphs, in this paper, we propose an algorithm named HFMG to discover novel and meaningful frequent patterns whose components are highly correlated with each other. HFMG mines two kinds of meaningful patterns efficiently according to which relationships we focus on. The effectiveness of the proposed algorithm is confirmed through the experiments with real and synthetic datasets.