資訊管理學報
李瑞庭;楊富丞;李偉誠;
頁: 161-184
日期: 2012/01
摘要: 目前，已有許多學者提出探勘頻繁一維區間樣式的方法。但是，在實務上有許多應用包括多維度區間的資料。因此，在本篇論文中，我們提出「MIAMI」演算法，它利用頻繁樣式樹，以深度優先法遞迴產生所有的封閉性多維度區間樣式。在探勘的過程中，我們設計三個有效的修剪策略，以刪除不可能的候選樣式，以及使用封閉性測試移除非封閉性樣式。實驗結果顯示，MIAMI 演算法比改良式Apriori 演算法更有效率，也更具擴充性。
關鍵字: 多維區間樣式;一維區間樣式;頻繁樣式;封閉性樣式;資料探勘;
Abstract: Many methods have been proposed to find frequent one-dimensional (1-D) interval patterns, where each event in the database is realized by a 1-D interval. However, the events in many applications are in nature realized by multi-dimensional intervals. Therefore, in this paper, we propose an efficient algorithm, called MIAMI, to mine closed multi-dimensional interval patterns from a database. The MIAMI algorithm employs a pattern tree to enumerate all closed patterns in a depth-first search manner. In the mining process, we devisethree effective pruning strategies to remove impossible candidates and perform a closure checking scheme to eliminate non-closed patterns. The experimental results show that the MIAMI algorithm is more efficient and scalable than the modified Apriori algorithm.
Keywords: multi-dimension interval pattern;1-dimension interval pattern;frequent pattern;closed pattern;data mining;
瀏覽次數: 19611 下載次數: 12383
引用 導入Endnote
頁: 161-184
日期: 2012/01
摘要: 目前，已有許多學者提出探勘頻繁一維區間樣式的方法。但是，在實務上有許多應用包括多維度區間的資料。因此，在本篇論文中，我們提出「MIAMI」演算法，它利用頻繁樣式樹，以深度優先法遞迴產生所有的封閉性多維度區間樣式。在探勘的過程中，我們設計三個有效的修剪策略，以刪除不可能的候選樣式，以及使用封閉性測試移除非封閉性樣式。實驗結果顯示，MIAMI 演算法比改良式Apriori 演算法更有效率，也更具擴充性。
關鍵字: 多維區間樣式;一維區間樣式;頻繁樣式;封閉性樣式;資料探勘;
探勘封閉性多維度區間樣式
Abstract: Many methods have been proposed to find frequent one-dimensional (1-D) interval patterns, where each event in the database is realized by a 1-D interval. However, the events in many applications are in nature realized by multi-dimensional intervals. Therefore, in this paper, we propose an efficient algorithm, called MIAMI, to mine closed multi-dimensional interval patterns from a database. The MIAMI algorithm employs a pattern tree to enumerate all closed patterns in a depth-first search manner. In the mining process, we devisethree effective pruning strategies to remove impossible candidates and perform a closure checking scheme to eliminate non-closed patterns. The experimental results show that the MIAMI algorithm is more efficient and scalable than the modified Apriori algorithm.
Keywords: multi-dimension interval pattern;1-dimension interval pattern;frequent pattern;closed pattern;data mining;
瀏覽次數: 19611 下載次數: 12383
引用 導入Endnote