| Summary: | 碩士 === 國立臺灣大學 === 資訊管理學研究所 === 98 === Mining spatial-temporal patterns can help us retrieve valuable and implicit information from an abundance of spatial-temporal data in a database. In this thesis, we propose a novel algorithm, STP-Mine (Spatial- Temporal Patterns-Mine), to mine closed stpatterns in a spatial-temporal database. The proposed algorithm consists of three phases. First, we find all frequent length-1 patterns (1-patterns) and construct a projected database for each frequent 1-pattern found. Second, we recursively generate frequent super-patterns in the spatial dimension in a depth-first search manner. Third, once a pattern cannot grow further in the spatial dimension, we extend it in the temporal dimension in a depth-first search manner. The steps in the second and third phases are repeated until no more frequent closed patterns can be found. During the mining process, we employ several effective pruning strategies to prune unnecessary candidates and a closure checking scheme to remove non-closed stpatterns. The experimental results show the STP-Mine algorithm is efficient and scalable, and outperforms the modified A-Close algorithm in one order of magnitude.
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