最近幾年，Wi-Fi越來越普及、涵蓋率越來越高，基於此，有許多研究提出利用Wi-Fi定位的的方法，但卻沒有研究考慮到因為用Wi-Fi定位時所產生的資料不確定性。因此，我們提出一個完整的方法從智慧型手機蒐集的Wi-Fi指紋，也就是智慧型手機在一個地點所能掃瞄到的各個Wi-Fi訊號強度，從中找出使用者每天的生活軌跡。我們在地點判別時考慮了資料不確定性，並使用機率性順序型樣探勘演算法來找出使用者軌跡。
我們的方法如下：一開始利用我們所提出的Wi-F距離公式來找出使用者可能去過的地點，接著利用我們所提出的Wi-Fi相似公式去辨別使用者去過的地點，並將它表示為不確定性資料模式，最後再提出一個演算法去探勘使用者的軌跡。我們做了一連串的實驗去評估我們的方法中的每個步驟，包含利用我們所提的Wi-Fi距離公式所做的分群、確定性資料模型及不確定性資料模型，實驗結果顯示我們在每個步驟都有很高的準確率。

Wi-Fi hot spots have quickly increased in recent years. Accordingly, discovering user positions by using Wi-Fi fingerprints has attracted much research attention. The Wi-Fi fingerprints are the sets of Wi-Fi scanning results recorded in mobile devices. However, the issue of data uncertainty is not considered in the proposed Wi-Fi positioning systems. In this paper, we propose a framework to find user trajectories in their daily life from the Wi-Fi fingerprints recorded in their smartphones. In this framework, we first discover places with the proposed Wi-Fi distance metric. Second, we propose two similarity functions to recognize the places and show the probabilities of the places where the user stayed in by our proposed uncertain data models. Final, an algorithm on probabilistic sequential pattern mining is used for finding user trajectories. A series of experiments are performed to evaluate each step of the framework. The experiment results reveal that the steps in our framework are all with high accuracy.

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