Plans or sequences of actions are an important form of data. With the proliferation of database technology, plan databases (or planbases) are increasingly common. E cient discovery of important patterns of actions in plan databases presents a challenge to data mining. In this paper, we present a method for mining signi cant patterns of successful actions in a large planbase using a divide-andconquer strategy. The method exploits multi-dimensional generalization of sequences of actions and extracts the inherent hierarchical structure and sequential patterns of plans at di erent levels of abstraction. These patterns are used in turn to subsequently narrow down the search for more speci c patterns. The process is analogous to the use of divide-and-conquer methods in hierarchical planning. We illustrate our approach using a travel planning database
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