隨著科技的進步，使得儲存在資料庫內的資料量大幅增加，於是如何從這些大量的資料中去找尋我們所要的資訊及知識，便成為我們所需探討的課題。然而，資料探勘 (data mining) 技術可以協助我們做到從這些資料中去挖掘出所需的資訊及知識，其中關聯法則 (association rule) 為最常使用的工具之一。但是，傳統的關聯法則演算法皆是著重在找出所有資料項目間存在的法則，這不僅耗費整個法則產出的時間，而且所產出的法則數量會相當龐大，使用者較難從其中去找尋到符合自己需求的法則。因此，本研究針對此缺失來做改進的動作，以有趣度 (interest) 這個判斷資料項目間是否為偶然發生的衡量指標作為基準，找出資料項目間的相關程度，以求得使用者所需求的法則，並會與目前唯一一個亦是運用有趣度當成衡量指標來挖掘關聯法則的 KORD (k-Optimal-Rule-Discovery) 演算法做比較，以達成率來表示本研究所提的IBR (Interest-Based-Rule) 演算法可達到多好的效果，而達成率的計算方式是以 IBR 演算法所得的法則有趣度平均值除以 KORD 演算法所得的法則有趣度平均值，此值最高為1。實證結果顯示，在考慮完整的修剪空間，以及 IBR 演算法本身的限制條件下，IBR 演算法會有遺失掉一些有趣度較高之關聯法則的風險，但在演算法的搜尋空間上可大幅減少，且達成率亦有不錯的表現，大部分的實證資料檔達成率皆在0.9以上，而最差的情況則只有0.53的達成率。

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中文

武家慶 (2001)，“適用於挖掘多層次聯結規則的架構”，逢甲大學資訊工程學系碩士班碩士論文。

張恭維 (2001)，“結合關聯法則與模糊叢聚之網際探勘架構”，元智大學資訊管理學系碩士班碩士論文。

劉泓郁 (2003)，“建構一個以模糊關聯法則為基礎之產品開發系統”，元智大學工業工程與管理研究所碩士論文。

英文

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