藉由資訊技術的支援，企業可以更輕易地蒐集到消費者的個人資料及旅遊過的景點資料，從這些快速累積的資料中，找出對旅遊業者有用的資訊與知識，即成為旅遊業者重要的問題之一。在本篇論文中，我們以消費者之個人特徵資料、及旅遊過之景點資料為探勘的資料來源，分別設計兩個方法來探勘旅遊景點最適性之消費者：首先，我們修改Apriori演算法來探勘個人特徵與景點之間的關聯規則，藉由關聯規則所顯示出的旅遊傾向，來發掘旅遊景點最適性之消費者。再者，我們以某一旅遊景點為探勘目標，修改前一方法來發掘此一旅遊景點最適性的消費者。從實驗評估中顯示，我們所提出之演算法可以較快速地找出所要的關聯規則。

With the support of information technology, an enterprise can easily collect data from both consumers' personal characteristics and visiting sports. How to discover the useful information and knowledge for travel agencies is one of the most important issues from the rapidly accumulated data. In this paper, we use consumers' personal characteristics and visiting spots as the source data of mining, and propose two methods to discover the most adaptive consumers of visiting spots. First, we modify the Apriori algorithm to mine association rules between personal characteristics and spots. According to the traveling inclination of the association rules, we can find the most adaptive consumers of the visiting spots. Moreover, we discuss one visiting spot as the target of mining, and modify the front method to find the most adaptive consumers of the visiting spot. The experiments show that the performances of both methods are faster than the modified algorithms for mining the association rules, respectively.