關聯挖掘的目的是從大量數據中發現對用戶有用、新穎、重要的關聯規則。傳統的關聯挖掘算法會產生大量對用戶而言顯而易見的平凡規則，使那些真正對用戶有用的新穎規則被淹沒，而一些針對新穎性的改進算法往往又存在先驗知識表達復雜且工作量極大的問題。在本文中，我們運用簡單的分類樹，引入“新穎度”的概念，對Apriori 算法進行改進，得到了基于“新穎度”的關聯挖掘算法，此算法既充分考慮了挖掘過程中得新穎性問題，又克服了先驗知識表達過于復雜的困難。
關鍵詞：關聯規則挖掘；Apriori 算法；新穎度

Abstract: The objective of mining association rules is to find useful, novel and important association rules from large database. Traditional association rule mining algorithm may often produce too many obvious and non-novel rules to user, making really novel and interesting rules submerged. Some former way to solve novel problem need a lot of extra work to preprocess the data. In this paper, we introduce the concept of “novelty” and improve the algorithm into a new association algorithm based on “novelty” to overcome the above problems.
Key words: Association Rule mining; Apriori Algorithm; Novelty