在本篇論文中，我們以消費者之旅遊資料為探勘的資料來源，每一筆旅遊資料記錄有消費者曾經旅遊過的景點與停留時間，我們以某一景點為探勘的目標，並視各其他景點為欲分類的屬性，利用分類分析(classification)來發掘此一景點最適性之旅遊行程。首先，我們只考量在旅遊資料中消費者曾經旅遊過的景點，然後針對旅遊資料進行分類分析，藉由所建立的決策樹，可得知那些屬性會影響是否旅遊過此一景點，以做為發掘包含有此一景點最適性之旅遊行程規劃的依據。再者，我們考量景點停留的時間、分別將每一景點分解成其停留時間數量的項目屬性，然後視分解後的其他項目屬性欲分類的屬性，並進行分類分析，藉由所建立的決策樹，可得知那些分解後的項目屬性會影響是否旅遊此一景點，以做為發掘包含有停留時間之此一景點最適性的旅遊行程。此探勘結果，對於旅遊業者擬定景點之行程規劃，可以提供非常有用的參考資訊

In this paper, we use consumers’ visiting data as the source of mining. Each visiting data records a consumer ever visited spots and stayed time. We let one spot as the target of mining and treat other spots as attributes for classification. Then we use classification analysis to discover the most adaptive trip pans of the spot. First, we only consider consumers ever visited spots in the visiting data, and classify the visiting data to construct a decision tree. We find some attributes may affect the spot to be visited according to the decision tree. It is the basis to discover the most adaptive trip plans of the spot. Moreover, we consider spots with staying time in the visiting data. Each spot is divided to t spot items where t is the quantity of the staying time, t is positive integer, and the quantities of staying time of these spot items are, respectively, from 1 to t. we treat other spot items after dividing as attributes for classification, and classify the visiting data to construct a decision tree. According to the decision tree, we find some attributes may affect the spot to be visited. It is the basis to discover the most adaptive trip plans with staying time for the spot. The results of the mining can provide very useful information for travel agencies to draft trip plans of spots.