周期共同位置樣式的探勘，可以發掘出在地理資訊上同存現象的周期性，並且它也是時空資料探勘上新穎和重要的問題，而隨著資訊科技的發展，時空的資料量也隨之快速的大量成長，然而目前並沒有技術是專門針對探勘周期共同位置樣式所設計，而使用周期樣式和共同位置樣式的技術會產生多餘的共同位置樣式和花費多餘的計算時間，多餘的共同位置樣式是不必要的資訊，且會導致過高的計算成本，因此我們定義了簡潔的最大高頻周期共同位置樣式（concise MFPCP）為了消除多餘的共同位置樣式，並且我們提出了multiple autocorrelation和MFPCP miner演算法，multiple autocorrelation演算法用於偵測可能存在的周期，並且保證找出完整的周期，而MFPCP miner演算法則更有效率的找出最大高頻周期共同位置樣式，實驗結果顯示我們所提出的方法能保證周期的完整性和提高其效率。

Periodic co-location pattern mining is a novel and important branch of the spatio-temporal data mining. With the evolving of computation and communication technology, the sizes of spatio-temporal datasets are growth extremely. However, no existing technique is particularly designed for mining periodic co-location patterns. In addition, applying the techniques of periodic pattern mining and co-location pattern mining to generate periodic co-location patterns directly will produce redundant patterns and result in higher computation cost. Therefore, we define the concise maximal frequent periodic co-location pattern (concise MFPCP) for pruning the redundant co-location pattern. Moreover, we propose a multiple autocorrelation algorithm to detect the periodicity, and a MFPCP miner algorithm to discover concise MFPCPs. The multiple autocorrelation algorithm guarantees that all possible periods will be found out. The MFPCP miner algorithm uses the property of MFPCP for discovering concise MFPCP more efficiently. Experimental results show that the proposed periodicity detection method has high accuracy without losing performance, and the proposed MFPCP mining method discovers MFPCP with high efficiency.

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