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    請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/8842


    題名: 時序資料庫中緊密頻繁連續事件型樣之有效探勘;ClosedPROWL: Efficient Mining of Closed Frequent Continuities in Temporal Databases
    作者: 林國瑞;Kuo-Zui Lin
    貢獻者: 資訊工程研究所
    關鍵詞: 型樣探勘;緊密頻繁連續事件;交易間關聯性探勘;資料探勘;Pattern Mining;Closed Frequent Continuities;Inter-Transaction Association Mining;Data Mining
    日期: 2004-07-09
    上傳時間: 2009-09-22 11:36:01 (UTC+8)
    出版者: 國立中央大學圖書館
    摘要: 在資料探勘的領域中,型樣探勘一直是個相當重要的課題。早期,大部分的研究如頻繁項目集,主要在找尋同一筆交易中項目間的關聯性。近來,為能更有效地預測分析資料庫的行為趨勢,學者開始將焦點集中在交易間關聯性之探勘,用來描述不同交易間項目彼此的關係。連續事件即為一種交易間關聯性型樣,其明確描述著不同交易之間的相對位置與前後順序等關係。由於連續事件跨越了交易記錄間的藩籬,以致於潛在型樣與規則的數量急遽增加,如此不但會降低整體演算法的效率,還會使探勘結果難以運用,因此我們選擇探勘緊密頻繁連續事件。緊密頻繁連續事件是一群具有代表性的頻繁連續事件,不但數量相對較少,且可以由其展開列舉出所有的頻繁連續事件,因此具有消除冗餘資訊又不喪失其完整性的優點。本篇論文中,我們提出一個有效率的演算法ClosedPROWL,主要採用投影視窗列表技術以進行緊密頻繁連續事件的探勘。實驗結果顯示,不論在合成資料集或真實資料集,相較於之前其他方法,我們的演算法皆擁有更佳的效能與延展性。 Mining frequent patterns in temporal databases is a fundamental and essential problem in data mining areas. Over the past few years a considerable number of studies have been made in frequent itemset mining, which consider only relationships among items in the same transaction. Recently, researchers began to focus the problem on the inter-transaction association that describes the association relationships among different transactions. A continuity is a kind of inter-transaction association which describes definite temporal relationships among different transactions. Since continuities breaks the barrier of transactions, the number of potential patterns will increase drastically. An alternative idea is to mine closed frequent continuities. Mining closed frequent patterns has the same power as mining the complete set of frequent patterns, while substantially reduce redundant rules to be generated and increase the effectiveness of mining. In this paper, we propose an efficient algorithm, ClosedPROWL, for closed frequent continuities mining by projected window list technology. Experimental evaluation on both real world and synthetic datasets shows that our algorithm is more efficient and scalable compared to previously proposed algorithm.
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