Mining Closed Multi-Dimensional Interval Patterns__臺灣人文及社會科學引文索引資料庫

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題名：	Mining Closed Multi-Dimensional Interval Patterns
書刊名：	資訊管理學報
作者：	李瑞庭／楊富丞／李偉誠
作者(外文)：	Lee, Anthony J. T.／Yang, Fu-chen／Lee, Wei-cheng
出版日期：	2012
卷期：	19:1
頁次：	頁161-184
主題關鍵詞：	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；Multi-dimension interval pattern；1-dimension interval pattern；Frequent pattern；Closed pattern；Data mining
原始連結：	連回原系統網址
相關次數：	被引用次數:期刊(1) 博士論文(0) 專書(0) 專書論文(0) 排除自我引用:1 共同引用:0 點閱:36

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。

以文找文

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.

以文找文

期刊論文
1.	Han, J.、Pei, J.、Yin, Y.、Mao, R.(2004)。Mining frequent patterns without candidate generation: a frequent pattern tree approach。Data Mining and Knowledge Discovery，8(1)，53-87。
2.	Allen, J. F.(1983)。Maintaining Knowledge about Temporal Intervals。Communications of the ACM，26(11)，832-843。
3.	Zaki, M. J.(2001)。SPADE: An efficient algorithm for mining frequent sequences。Machine Learning，42(1/2)，31-60。
4.	Winarko, E.、Roddick, J.F.(2007)。ARMADA - An algorithm for discovering richer relative temporal association rules from interval-based data。Data and Knowledge Engineering，63(1)，76-90。
5.	Wu, S.-Y.、Chen, Y.-L.(2007)。Mining nonambiguoustemporalpatterns for interval-based events。IEEE Transactions on Knowledge and Data Engineering，19(6)，742-758。
6.	Wu, S.-Y.、Chen, Y.-L.(2009)。Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events。Data and Knowledge Engineering，68(11)，1309-1330。
7.	Zaki, M. J.、Hsiao, C.-J(2005)。Efficient algorithms for mining closed itemsets and their lattice stracture。IEEE Transactions on Knowledge and Data Engineering，17(4)，462-478。
8.	Amo, S. de、Junior, W.P.、Giacometti, A.(2008)。MILPRIT*: A constraint-based algorithm for mining temporal relational patterns。International Journal of Data Warehousing and Mining，4(4)，42-61。
9.	Kong, X.、Wei, Q.、Chen, G.(2010)。An approach to discovering multi-temporal patterns and its application to financial databases。Information Sciences，180(6)，873-885。
10.	Lee, Y.J.、Lee, J.W.、Chai, D.J.、Hwang, B.H.、Ryu, K.H.(2009)。Mining temporal interval relational rules from temporal data。The Journal of Systems and Software，82(1)，155-167。
11.	Papapetrou, P.、Kollios, G.、Sclaroff, S.、Gunopulos, D.(2009)。Mining frequent arrangements of temporal intervals。Knowledge and Information Systems，21(2)，133-171。

會議論文
1.	Srikant, R.、Agrawal, R.(1995)。Mining Sequential Patterns。The Eleventh International Conference on Data Engineering。Taipei：IEEE Computer Society。3-14。
2.	Han, J.、Pei, J.、Mortazavi-Asl, B.、Chen, Q.、Dayal, U.、Hsu, M. C.(2000)。FreeSpan: Frequent Pattern-Projected Sequential Pattern Mining。ACM SIGKDD International Conference on Knowledge Discovery in Databases，355-359。
3.	Pei, J.、Han, J.、Mortazavi-Asl, B.、Pinto, H.、Chen, Q.、Dayal, U.、Hsu, M. C.(2001)。PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth。17th International Conference on Data Engineering，215-224。
4.	Agrawal, R.、Srikant, R.(1994)。Fast algorithms for mining association rules in large database。The 20th International Conference on Very Large Data Bases。Morgan Kaufmann Publishers Inc.。478-499。
5.	Guyet, T.、Quiniou, R.(2008)。Mining temporal patterns with quantitative intervals218-227。
6.	Hoppner, F.(2001)。Learning temporal rules from state sequences。Seattle, USA。25-31。
7.	Kam, P.-S.、Fu, A. W.-C.(2000)。Discovering temporal patterns for interval-basedevents。London, UK。317-326。
8.	Patel, D.、Hsu, W.、Lee, M.(2008)。Mining relationships among interval-based events for classification。Vancouver, Canada。393-404。
9.	Pei, J.、Han, J.、Mao, R.(2000)。CLOSET: An efficient algorithm for mining frequent closed itemsets。Dallas, USA。11-20。

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