在此論文中，我們針對如何有效探勘時序性常見資料項集合提出探討。本論文將探勘時序性常見資料項集合過程分成兩個階段：先探勘單一時間點的最大常見資料項集合，再以這些資料項集合為資料單位，探勘出時序性最大常見資料項集合。我們設計出資料項出現計數矩陣，根據計數矩陣中的數值，可加速可能的常見資料項集合之組合過程，並利用資料項索引序列快速計算出各資料項集合的出現次數，這些資料結構並擴展以運用在時序性常見資料項集合之探勘。此外，我們以各最大常見資料項集合的間隔區間中的最大值當作探勘區間大小，可確保找出精簡且完整的時序性常見資料項集合。實驗結果顯示我們所提出之演算法比相關文獻中所提出的方法，在執行時間上更有效率。

Most of the previous studies on mining frequent item sets overlooks time components , which are usually attached to transactions in databases .This result in the loss of the chance to discover some meaningful time-related frequent item sets .
Mining temporal frequent item sets poses more challenge on efficient processing than the mining of traditional frequent item set because he number of potential temporal frequent item sets becomes extremely large with varying sliding window of temporal frequent item sets.
In this paper we proposed an efficient strategy to generate all potential temporal frequent item sets, and using bit strings to mine frequent item sets. In other papers , the size of sliding window must be defined before mining , in our paper we use the maximum sliding interval as sliding window to avoiding undesirable initializing of sliding window.

[1] Ming-Syan Chen ,Jiawei Han , and Philips S Yu ,”Data Mining :An Overview from Database Perspective” IEEE Transaction on Knowledge and Data Engineering , vol. 8, NO .6 ,1996
[2] R Agrawal and R.Srikant ,”Fast Algorithm for Mining Association Rules in Large Databases”, in Proc.20th International Conference on Very Large DataBases,1994.
[3] J. Ma , B . Knight and T .Peng ,“Representing Temporal Relationship Events and Their Effects”, in Proc. of the 4th International Workshop Temporal Representation and Reasoning (TIME’97),1997
[4] Mehmet A. Orgun , “A Temporal Relationship Algebra Based on Multiple Time-Lines” , in Proc. of the Sixth International Workshop Temporal Representation and Reasoning (TIME’99),1999
[5] Christian S Jensen and Richard T. Sondgrass “Temporal Data Management ”, IEEE Transaction on Knowledge and Data Engineering , vol. 11, NO .1 ,1999
[6] Jiawei Han ,Wan Gong and Yiwen Yin , “Mining Segment -Wise Periodic Patterns in Time-Related Databases” , in Proc. of 1998 International Conference on Knowledge Discovery and Data Mining(KDD’98),1998
[7] Jiawei Han , Guozhu Dong and Yiwen Yin , “Efficient Mining of Partial Periodic Patterns in Time Series Database ” , in Proc. of the 15th International Conference on Data Engineering,1999
[8] Vanbthi Gopalalakrishnan and Bruce G. Buchanan ,”Representing and Learning Temporal Relationship among Experimental Variables” , in Proc. Fifth International Workshop on Temporal Representation and Reasoning ,1998
[9] Xinfeng Ye and John A .Keane , “Mining Association Rules in Temporal Databases”, in Proc. Of 16th International Conference on Data Engineering , 1998
[10] Ling Feng , Hongjun Lu and Jeffrey Xu Yu , “Mining Inter-Transaction Associations with Templates” , 1999 ACM CIKM International Conference on Information and Knowledge Management (CIKM99),1999
[11] Claudio Bettiniand X . Sean Wang ,”Discovering Frequent Event Patterns with Multiple Granularity in Time Sequences” , IEEE Transactions on Knowledge and Data Engineering , vol. 10, NO. 2 ,1998
[12] H. Lu , J. Han and L. Feng “Stock movement Predication And N-Dimensional Inter-Transaction Association Rules ” , 1998 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery ,1998
[13] Anthony K.H .Tung ,Hongjun Lu and Jiawei Han , “Breaking the Barrier of Transactions : Mining Inter-transaction Association Rules” , in Proc. Knowledge Discovery and Data Mining (KDD’99),1999