The hyperclique pattern is a new type of association pattern, in which items are highly affiliated with each other. The presence of an item in one transaction strongly implies the presence of every other item in the same hyperclique pattern. The maximal hyperclique pattern is a more compact representation of a group of hyperclique patterns, which is desirable for many applications. The standard algorithms mining the two kinds of patterns are different. This paper presents a fast algorithm called hybrid hyperclique pattern growth (HHCP-growth) based on FP-tree (frequent pattern tree), which unifies the mining processes of the two patterns. This algorithm adopts recursive mining method and exploits many efficient pruning strategies. Some propositions are also presented and proved to indicate the effectiveness of the strategies and the validity of the algorithm. The experimental results show that HHCP-growth is more effective than the standard hyperclique pattern and maximal hyperclique pattern mining algorithms, particularly for large-scale datasets or at low levels of support.