臺灣博碩士論文加值系統

在此論文中，我們討論了具興趣時段感知之頻繁與循序樣式資料探勘的問題。我們發現使用者對較新的資料比過去的資料更感興趣。若是能考慮使用者的興趣時段，我們便能夠獲得在交易資料庫中最有趣的頻繁樣式或在序列資料庫中的循序樣式。
我們探討了在時間資料庫中發掘相關性的通用模型，在這模型中資料的生存週期可以允許有所不同。為了解決這個問題，我們提出了一種有效的演算法Twain，以便找出頻繁樣式更為精確的頻繁時段。Twain不僅能產生頻繁模式更精確的頻繁時段，也發現了更多有趣的頻繁模式。
另外，我們提出了一個循序樣式探勘中的通用模型，處理的資料庫為漸進式的資料庫，而資料庫中的資料可能是靜態的、可被新增的或可被刪除的。此外，我們也提出了一個漸進式的演算法Pisa，逐步在使用者的興趣時段中找尋循序樣式。Pisa採用了一個漸進式循序樹，能夠有效地保留最新的資料序列，並產生最新且完整的循序樣式，同時刪除過時的資料和相對應的循序樣式。
最後，我們討論了在前述通用模型中必定存在的可擴展性問題。當資料庫擁有越來越多的序列或使用者的興趣時段加大時，用來處理漸進式循序樣式的時間和空間會急劇增加。由於在單一處理器上計算能力和工作空間有限，通常很難不停地擴大。因此，我們設計了一個分散式的演算法DPSP以處理大量的資料。在每一個時間點，DPSP能夠刪除過時的資料、更新目前的循序樣式和產生在最新的興趣時段中頻繁出現的循序樣式。

In this dissertation, we addressed the frequent and sequential pattern mining problem with period of interest awareness. It is noted that users are usually more interested in recent data than old ones. Taking the period of interest into consideration, we are able to derive most interesting frequent patterns in time domain in a transaction database or sequential patterns in a sequence database.
We first explored the general model of mining associations in a temporal database, where exhibition periods of items are allowed to be different from one to another. To address this issue, we proposed an efficient algorithm Twain, standing for TWo end AssocIation miNer to give more precise frequent exhibition periods of frequent temporal itemsets. Twain not only generates frequent patterns with more precise frequent exhibition periods, but also discovers more interesting frequent patterns.
We also proposed a general model of sequential pattern mining with a progressive database while the data in the database may be static, inserted or deleted. In addition, we presented a progressive algorithm Pisa, standing for Progressive mIning of Sequential pAtterns, to progressively discover sequential patterns in a defined period of interest. Pisa utilizes a progressive sequential tree to efficiently maintain the latest data sequences, discover the complete set of up-to-date sequential patterns, and delete obsolete data and patterns accordingly.
In addition, we examined the intrinsic scalability problem of mining progressive sequential patterns. When the number of sequences grows and the POI becomes larger, the time and space used to conduct progressive sequential patterns increases dramatically. Due to the limited computing power and working space, single processors usually struggle to scale up. Therefore, we designed a distributed algorithm DPSP, standing for Distributed Progressive Sequential Pattern mining algorithm, to deal with large amounts of data. At each timestamp, DPSP is able to delete obsolete itemsets, update current candidate sequential patterns and report up-to-date frequent sequential patterns within the current POI.

1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Overview of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Two-End Association Miner with Precise Frequent Exhibition Periods . . . . . 3
1.2.2 A General Model for Sequential Pattern Mining with a Progressive Database . 3
1.2.3 Distributed Progressive SequentialPatternMiningontheCloud . . . . . . . . 4
1.3 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Two-End Association Miner with Precise Frequent Exhibition Periods 6
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.2 RelatedWorks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2.3 AprioriIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.4 SP F . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3 Twain for Precise General Temporal Association . . . . . . . . . . . . . . . . . . . . . 24
2.3.1 Algorithm T wain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.3.2 On Detailed Operations of Twain . . . . . . . . . . . . . . . . . . . . . . . . 28
2.3.3 Correctness of Algorithm Twain . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.3.4 Incremental Ability of Twain . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.4.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.4.2 ExecutionTime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.4.3 I/O Costs and CPU Overheads . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.4.4 Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3 A General Model for Sequential Pattern Mining with a Progressive Database 41
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.2.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2.2 RelatedWorks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.2.3 Comparative Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.3 Progressive mIning of Sequential pAtterns . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3.1 PS-tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.3.2 Algorithm Pisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.3.3 On Maintaining the PS-tree . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.3.4 Fast Pisa with Approximate Results . . . . . . . . . . . . . . . . . . . . . . . 61
3.3.5 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.4.1 ExperimentDesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.4.2 Cumulative Execution Time . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.4.3 The Effects of the Input Parameters . . . . . . . . . . . . . . . . . . . . . . . 69
3.4.4 Scalability Issue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3.4.5 Space Issue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.4.6 Practicability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
3.4.7 The Benefits of Fast Version of Pisa . . . . . . . . . . . . . . . . . . . . . . . 73
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4 Distributed Progressive Sequential Pattern Mining on the Cloud 76
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.2.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.2.2 DDM and Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.2.3 RelatedWorks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.3 Distributed Progressive Sequential Pattern Mining . . . . . . . . . . . . . . . . . . . . 83
4.3.1 Candidate Computing Job . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.3.2 Support Assembling Job . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.4.1 Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.4.2 Distributed Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5 Conclusions 97
Bibliography 99

[1] R. Agarwal, C. Aggarwal, and V. Prasad. A Tree Projection Algorithm for Generation of Frequent
Itemsets. Jornal of Parallel and Distributed Computing (Special Issue on High Performance
Data Mining), 2000.
[2] R. Agrawal, T. Imielinski, and A. Swami. Mining Association Rules between Sets of Items in
Large Databases. Proceedings of ACM SIGMOD International Conference on Management of
Data, pages 207–216, May 1993.
[3] R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules. Proceedings of the
20th International Conference on Very Large Data Bases (VLDB94), pages 478–499, September
1994.
[4] R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules in Large Databases.
Proceedings of the 20th International Conference on Very Large Data Bases (VLDB94), pages
478–499, September 1994.
[5] R. Agrawal and R. Srikant. Mining Sequential Patterns. Proceedings of the 11th IEEE International
Conference on Data Engineering (ICDE95), pages 3–14, February 1995.
[6] J. Ale and G. Rossi. An Approach to Discovering Temporal Association Rules. ACMSymposium
on Applied Computing, 2000.
[7] S. Aseervatham, A. Osmani, and E. Viennet. bitSPADE: A Lattice-Based Sequential Pattern
Mining Algorithm Using Bitmap Representation. Proceedings of the 6th IEEE International
Conference on Data Mining (ICDM06), 2006.
[8] A. M. Ayad, N. M. El-Makky, and Y. Taha. Incremental Mining of Constrained Association
Rules. Proceedings of the 1st SIAM Conference on Data Mining (SDM01), 2001.
[9] J. Ayres, J. Gehrke, T. Yiu, and J. Flannick. Sequential PAttern Mining using A Bitmap Representation.
Proceedings of 8th ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining, pages 429–435, July 2002.
[10] Z. J. Bai. A Parallel Algorithm for Computing the Generalized Singular Value Decomposition.
Journal of Parallel and Distributed Computing, 20(3):280–288, 1994.
[11] A. Balachandran, G. M. Voelker, P. Bahl, and P. V. Rangan. Characterizing User Behavior and
Network Performance in a PublicWireless Lan. Proceedings of ACM SIGMETRICS, June 2002.
[12] C. Besemann and A. Denton. Integration of Profile Hidden Markov Model Output into Association
Rule Mining. Proceedings of the 11th ACM SIGKDD International Conference on
Knowledge Discovery in Data Mining, pages 538–543, 2005.
[13] C. Bettini, X. Wang, and S. Jajodia. Mining Temporal Relationships with Multiple Granularities
in Time Sequences. Bulletin of the IEEE Computer Society Technical Committee on Data
Engineering, 1998.
[14] J. Blanchard, F. Guillet, R. Gras, and H. Briand. Using Information-Theoretic Measures to
Assess Association Rule Interestingness. Proceedings of the 5th SIAM International Conference
on Data Mining (SDM05), 2005.
[15] H. Cao, N.Mamoulis, and D.W. Cheung. Mining Frequent Spatio-temporal Sequential Patterns.
Proceedings of 5th IEEE International Conference on Data Mining (ICDM05), pages 82–89,
November 2005.
[16] C.-Y. Chang, M.-S. Chen, and C.-H. Lee. Mining General Temporal Association Rules for Items
with Different Exhibition Periods. Proceedings of the 2nd IEEE International Conference on
Data Mining (ICDM02), December 2002.
[17] L. Chang, T. Wang, D. Yang, and H. Luan. SeqStream: Mining Closed Sequential Patterns
over Stream Sliding Windows. Proceedings of the 8th IEEE International Conference on Data
Mining (ICDM08), 2008.
[18] G. Chen, X. Wu, and X. Zhu. Sequential Pattern Mining in Multiple Streams. Proceedings of
the 5th International Conference on Data Mining (ICDM05), pages 585–588, 2005.
[19] J. Chen, H. He, G. Williams, and H. Jin. Temporal Sequence Associations for Rare Events.
Proceedings of the 8th Pacific Asia Conference on Knowledge Discovery and Data Mining
(PAKDD04), 2004.
[20] M.-S. Chen, J. Han, and P. S. Yu. Data Mining: An Overview from Database Perspective. IEEE
Transactions on Knowledge and Data Engineering, 5(1):866–883, Dec. 1996.
[21] M.-S. Chen, J.-S. Park, and P. S. Yu. Efficient Data Mining for Path Traversal Patterns. IEEE
Transactions on Knowledge and Data Engineering, 10(2):209–221, April 1998.
[22] X. Chen and I. Petr. Discovering Temporal Association Rules: Algorithms, Language and System.
Proceedings of the 16th IEEE International Conference on Data Engineering (ICDE00),
2000.
[23] X. Chen, I. Petrounias, and H. Heathfield. Discovery of Association Rules in Temporal Databases.
Proceedings of Issues and Applications of Database Technology, 1998.
[24] H. Cheng, P.-N. Tan, J. Sticklen, andW. F. Punch. Recommendation viaQueryCenteredRandom
Walk on K-Partite Graph. Proceedings of the 7th IEEE International Conference on DataMining
(ICDM07), pages 457–462, 2007.
[25] H. Cheng, X. Yan, and J. Han. IncSpan: Incremental Mining of Sequential Patterns in Large
Database. Proceedings of 10th ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining, pages 527–532, 2004.
[26] J. Chilson, R. Ng, A. Wagner, and R. Zamar. Parallel Computation of High Dimensional Robust
Correlation and Covariance Matrices. Proceedings of 10th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, pages 533–538, August 2004.
[27] E. Cohen, M. Datary, S. Fujiwaraz, A. Gionisx, P. Indyk, R. Motwanik, J. D. Ullman, and
C. Yangyy. Finding Interesting Associations without Support Pruning. IEEE Transactions on
Knowledge and Data Engineering, pages 64–78, 2001.
[28] S. Cong, J. Han, and D. Padua. Parallel Mining of Closed Sequential Patterns. Proceedings of
11st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages
562–567, August 2005.
[29] J. Dean and S. Ghemawat. MapReduce: Simplified DataProcessing on Large Clusters. Symposium
on Operating System Design and Implementation, 2004.
[30] I. Dhillon and D.Modha. A Data-clustering Algorithm on DistributedMemoryMultiprocessors.
Proceedings of the ACM SIGKDD Workshop on High Performance Knowledge Discovery, pages
245–260, 1999.
[31] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurasamy. Advances in Knowledge
Discovery and Data Mining. MIT Press, Cambridge, MA, 1996.
[32] M. N. Garofalakis, R. Rastogi, and K. Shim. SPIRIT: Sequential Pattern Mining with Regular
Expression Constraints. Proceedings of the 25th International Conference on Very Large Data
Bases (VLDB99), pages 223–234, 1999.
[33] B. Goethals, W. L. Page, and H. Mannila. Mining Association Rules of Simple Conjunctive
Queries. Proceedings of the 8th SIAM International Conference on Data Mining (SDM08),
2008.
[34] J.-K. Guo, B.-J. Ruan, and Y.-Y. Zhu. A Top-down Algorithm for Web Log Sequential Pattern
Mining. Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data
Mining (PAKDD05), 2005.
[35] Hadoop. http://hadoop.apache.org.
[36] J. Han and Y. Fu. Discovery of Multiple-Level Association Rules from Large Databases. Proceedings
of the 21st International Conference on Very Large Data Bases (VLDB95), pages 420–
431, September 1995.
[37] J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000.
[38] J. Han and J. Pei. Mining Frequent Patterns by Pattern-Growth: Methodology and Implications.
ACM SIGKDD Explorations (Special Issue on Scaleble Data Mining Algorithms), December
2000.
[39] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M.-C. Hsu. FreeSpan: Frequent
Pattern-projected Sequential Pattern Mining. Proceedings of the 6th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, pages 355–359, August 2000.
[40] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. C. Hsu. FreeSpan: Frequent
Pattern-projected Sequential Pattern Mining. Proceedings of the 6th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, pages 355–359, 2000.
[41] J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. Proceedings
of ACMSIGMOD International Conference onManagement of Data, pages 486–493,May 2000.
[42] S. K. Harms and J. S. Deogun. Sequential Association Rule Mining with Time Lags. Journal of
Intelligent Informatics Systems, 2004.
[43] Y. Hirate and H. Yamana. Sequential Pattern Mining with Time Interval. Proceedings of the
10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD06), pages
775–779, 2006.
[44] C.-C. Ho, H.-F. Li, F.-F. Kuo, and S.-Y. Lee. Incremental Mining of Sequential Patterns over a
Stream Sliding Window. Proceedings of IEEE International Workshop on Mining Evolving and
Streaming Data (IWMESD06), December 2006.
[45] J.-W. Huang, C.-Y. Tseng, J.-C. Ou, and M.-S. Chen. A General Model for Sequential Pattern
Mining with a Progressive Database. IEEE Transactions on Knowledge and Data Engineering,
20(9):1153–1167, 2008.
[46] A. Inokuchi and T. Washio. A Fast Method to Mine Frequent Subsequences from Graph Sequence
Data. Proceedings of the 8th IEEE International Conference on Data Mining (ICDM08),
2008.
[47] W. L. D. IV, P. Schwarz, and E. Terzi. Finding Representative Association Rules from Large Rule
Collections. Proceedings of the 9th SIAM International Conference on Data Mining (SDM09),
2009.
[48] N. Jiang and L. Gruenwald. An Efficient Algorithm to Mine Online Data Streams. Proceedings
of 2006 ACM SIGKDD Workshop on Theory and Practice of Temporal Data Mining, 2006.
[49] C. Jones, J. Hall, and J. Hale. Secure Distributed Database Mining: Principles of Design. Advances
in Distributed and Parallel Knowledge Discovery, pages 277–294, 2000.
[50] H. Kargupta, K. Das, and K. Liu. Multi-Party, Privacy-Preserving Distributed DataMining using
a Game Theoretic Framework. In Proceedings of the 11th European Conference on Principles
and Practice of Knowledge Discovery in Databases (PKDD07), pages 523–531, 2007.
[51] KDDCUP07. http://www.cs.uic.edu/ liub/netflix-kdd-cup-2007.html.
[52] Y. Ke, J. Cheng, andW. Ng. MIC Framework: An Information-Theoretic Approach to Quantitative
Association Rule Mining. Proceedings of the 22nd IEEE International Conference on Data
Engineering (ICDE06), 2006.
[53] D. Kifer, C. Bucila, J. Gehrke, and W. White. DualMiner: A Dual-Pruning Algorithm for Itemsets
with Constraints. Proceedings of the 8th ACMSIGKDD International Conference on Knowledge
Discovery and Data Mining, 2002.
[54] L. Lakshmanan, R. Ng, J. Han, and A. Pang. Exploratory Mining and Pruning Optimization of
Constrained Associations Rules. Proceedings of ACM SIGMOD International Conference on
Management of Data, 1998.
[55] L. V. S. Lakshmanan, R. Ng, J. Han, and A. Pang. Optimization of Constrained Frequent Set
Queries with 2-Variable Constraints. Proceedings of ACM SIGMOD International Conference
on Management of Data, pages 157–168, June 1999.
[56] C.-H. Lee, C.-R. Lin, and M.-S. Chen. On Mining General Temporal Association Rules in a
Publication Database. Proceedings of the 1st IEEE International Conference on Data Mining
(ICDM01), November 2001.
[57] C.-H. Lee, C.-R. Lin, and M.-S. Chen. Sliding-Window Filtering: An Efficient Algorithm for
IncrementalMining. Proceedings of the 10th ACMInternational Conference on Information and
Knowledge Management (CIKM01), November 2001.
[58] D. G. Lee. Coactive Learning for Distributed Data Mining. Proceedings of the 4th ACMSIGKDD
International Conference on Knowledge Discovery and Data Mining, pages 209–213, 1998.
[59] C. K.-S. Leung, M. A. F. Mateo, and D. A. Brajczuk. A Tree-Based Approach for Frequent Pattern
Mining from Uncertain Data. Proceedings of the 12th Pacific Asia Conference on Knowledge
Discovery and Data Mining (PAKDD08), 2008.
[60] C. Li and J. Wang. Efficiently Mining Closed Subsequences with Gap Constraints. Proceedings
of the 8th SIAM International Conference on Data Mining (SDM08), 2008.
[61] C.-R. Lin, C.-H. Yun, and M.-S. Chen. Utilizing Slice Scan and Selective Hash for Episode
Mining. Proceedings of 2001 ACM SIGKDD Workshop on Theory and Practice of Temporal
Data Mining, August 2001.
[62] J.-L. Lin and M. Dunham. Mining Association Rules: Anti-Skew Algorithms. Proceedings of
the 14th IEEE International Conference on Data Engineering (ICDE98), pages 486–493, 1998.
[63] M.-Y. Lin and S.-Y. Lee. Incremental Update on Sequential Patterns in Large Databases by
Implicit Merging and Efficient Counting. Information System, 29(5):385–404, July 2004.
[64] B. Liu, W. Hsu, and Y. Ma. Mining Association Rules with Multiple Minimum Supports. Proceedings
of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data
Mining, August 1999.
[65] D. Lo, S.-C. Khoo, , and J. Li. Mining and Ranking Generators of Sequential Patterns. Proceedings
of the 8th SIAM International Conference on Data Mining (SDM08), 2008.
[66] C. Luo and S. M. Chung. Efficient Mining of Maximal Sequential Patterns Using Multiple
Samples. Proceedings of the 5th SIAM International Conference on Data Mining (SDM05),
2005.
[67] P. Luo, H. Xiong, K. Lu, and Z. Shi. Distributed Classification in Peer-to-peer Networks. Proceedings
of the 13th ACMSIGKDD International Conference on Knowledge Discovery and Data
Mining, pages 968–976, 2007.
[68] H. Mannila, H. Toivonen, and A. I. Verkamo. Discovery of Frequent Episodes in Event Sequences.
Data Mining and Knowledge Discovery, 1(3):259–289, 1997.
[69] A. Marascu and F. Masseglia. Mining Sequential Patterns from Temporal Streaming Data. Proceedings
of the 1st ECML/PKDD Workshop on Mining Spatio-Temporal Data (MSTD05), October
2005.
[70] A. Marascu and F. Masseglia. Mining Sequential Patterns from Data Streams: a Centroid Approach.
Journal of Intelligent Information Systems, 27(3):291–307, November 2006.
[71] F. Masseglia, P. Poncelet, and M. Teisseire. Incremental Mining of Sequential Patterns in Large
Databases. Data and Knowledge Engineering, 46:97–121, July 2003.
[72] B. Mozafari, H. Thakkar, and C. Zaniolo. Verifying and Mining Frequent Patterns from Large
Windows over Data Streams. Proceedings of the 24th IEEE International Conference on Data
Engineering (ICDE08), 2008.
[73] A. Mueller. Fast Sequential and Parallel Algorithms for Association Rule Mining: A Comparison.
Technical Report CS-TR-3515, Dept. of Computer Science, Univ. of Maryland, College
Park, MD, 1995.
[74] B. G. J. Muhonen and H. Toivonen. Mining Non-Derivable Association Rules. Proceedings of
the 5th SIAM International Conference on Data Mining (SDM05), 2005.
[75] R. Ng and J. Han. Efficient and Effective ClusteringMethods for Spatial Data Mining. Proceedings
of the 20th International Conference on Very Large Data Bases (VLDB94), pages 144–155,
September 1994.
[76] S. Nguyen, X. Sun, andM. Orlowska. Improvements of IncSpan: IncrementalMining of Sequential
Patterns in Large Database. Proceedings of the 9th Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD05), 2005.
[77] J.-Z. Ouh, P. Wu, and M.-S. Chen. Constrained Based Sequential Pattern Mining. Proceedings
of International Workshop on Web Technology, December 2001.
[78] A. B. Pandey, J. Srivastava, and S. Shekhar. Web Proxy Server with Intelligent Prefetcher for
Dynamic Pages Using Association Rules. University of Minnesota Technical Report Number:
01-004, January 2001.
[79] A. B. Pandey, R. R. Vatsavai, X. Ma, J. Srivastava, and S. Shekhar. Data Mining for Intelligent
Web Prefetching. Proceedings of the Workshop on Mining Data Across Multiple Customer
Touchpoints for CRM (MDCRM02), May 2002.
[80] J.-S. Park, M.-S. Chen, and P. S. Yu. Using a Hash-Based Method with Transaction Trimming
for Mining Association Rules. IEEE Transactions on Knowledge and Data Engineering, 9(5):
813–825, October 1997.
[81] S. Parthasarathy, M. J. Zaki, M. Ogihara, and S. Dwarkadas. Incremental and Interactive SequenceMining.
Proceedings of the 8th International Conference on Information and Knowledge
Management (CIKM99), pages 251–258, 1999.
[82] J. Pei and J. Han. Can We Push More Constraints into Frequent Pattern Mining? Proceedings
of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,
August 2000.
[83] J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. PrefixSpan:
Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. Proceedings of the
17th IEEE International Conference on Data Engineering (ICDE01), 2001.
[84] J. Pei, J. Han, H. Pinto, Q. Chen, U. Dayal, and M. C. Hsu. PrefixSpan: Mining Sequential Patterns
Efficiently by Prefix-projected Pattern Growth. Proceedings of the 17th IEEE International
Conference on Data Engineering (ICDE01), pages 215–224, 2001.
[85] J. Pei, J. Han, and W. Wang. Mining Sequential Patterns with Constraints in Large Databases.
Proceedings of the 11th ACM International Conference in Information and Knowledge Management
(CIKM02), 2002.
[86] X.-H. Phan, L.-M. Nguyen, T.-B. Ho, and S. Horiguchi. Improving Discriminative Sequential
Learning with Rare-but-Important Associations. Proceedings of the 10th ACM SIGKDD International
Conference on Knowledge Discovery in Data Mining, 2005.
[87] C. Raissi, P. Poncelet, and M. Teisseire. SPEED: Mining Maximal Sequential Patterns over
Data. Proceedings of the 3rd IEEE International Conference Intelligent Systems (IS06), pages
546–552, 2006.
[88] C. Romero, S. Ventura, J. A. Delgado, and P. D. Bra. Personalized Links Recommendation
Based on Data Mining. In Adaptive Educational Hypermedia Systems. Proceedings of the 2nd
European Conference on Technology Enhanced Learning, September 2007.
[89] A. Savasere, E. Omiecinski, and S. Navathe. An Efficient Algorithm for Mining Association
Rules in Large Databases. Proceedings of the 21st International Conference on Very Large Data
Bases (VLDB95), pages 432–444, September 1995.
[90] B. W. Scotney, S. I. McClean, and M. C. Rodgers. Optimal and Efficient Integration of Heterogeneous
Summary Tables in a Distributed Database. Data and Knowledge Engineering, 29(3):
337–350, 1999.
[91] J. Soo, M.-S. Chen, and P. S. Yu. Efficient Parallel Data Mining for Association Rules. Proceedings
of the 4th ACM International Conference on Information and Knowledge Management
(CIKM95), pages 31–36, November 1995.
[92] R. Srikant and R. Agrawal. Mining Generalized Association Rules. Proceedings of the 21st International
Conference on Very Large Data Bases (VLDB95), pages 407–419, September 1995.
[93] R. Srikant and R. Agrawal. Mining Quantitative Association Rules in Large Relational Tables.
Proceedings of ACM SIGMOD International Conference on Management of Data, 1996.
[94] R. Srikant and R. Agrawal. Mining Sequential Patterns: Generalizations and Performance Improvements.
Proceedings of the 5th International Conference on Extending Database Technology
(EDBT96), March 1996.
[95] M. Steinbach, P.-N. Tan, and V. Kumar. Support Envelopes: A Technique for Exploring the
Structure of Association Patterns. Proceedings of the 10th ACM SIGKDD International Conference
on Knowledge Discovery in Data Mining, 2005.
[96] Szymon and Jaroszewicz. Polynomial Association Rules with Applications to Logistic Regression.
Proceedings of the 11st ACM SIGKDD International Conference on Knowledge Discovery
in Data Mining, 2006.
[97] S. K. Tanbeer, C. F. Ahmed, B.-S. Jeong, and Y.-K. Lee. CP-tree: A Tree Structure for Single-
Pass Frequent Pattern Mining. Proceedings of the 12th Pacific Asia Conference on Knowledge
Discovery and Data Mining (PAKDD08), 2008.
[98] A. Tansel and N. Ayan. Discovery of Association Rules in Temporal Databases. Proceedings of
AAAI on Knowledge Discovery in Databases, 1998.
[99] H. Toivonen. Sampling Large Databases for Association Rules. Proceedings of the 22nd International
Conference on Very Large Data Bases (VLDB96), pages 134–145, September 1996.
[100] H. Wang, X. Zhang, and G. Chen. Mining a Complete Set of Both Positive and Negative Association
Rules from Large Databases. Proceedings of the 12th Pacific Asia Conference on
Knowledge Discovery and Data Mining (PAKDD08), 2008.
[101] J. Wang and J. Han. BIDE: Efficient Mining of Frequent Closed Sequences. Proceedings of the
20th IEEE International Conference on Data Engineering (ICDE04), pages 79–91, 2004.
[102] K. Wang, Y. He, and J. Han. Mining Frequent Itemsets Using Support Constraints. Proceedings
of the 26th International Conference on Very Large Data Bases (VLDB00), September 2000.
[103] K. Wang, Y. Xu, and J. X. Yu. Scalable Sequential Pattern Mining for Biological Sequences.
Proceedings of the 13th ACM International Conference on Information and Knowledge Management
(CIKM04), pages 178–187, November 2004.
[104] X. Xu, N. Yuruk, Z. Feng, and T. A. J. Schweiger. SCAN: A Structural Clustering Algorithm
for Networks. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, pages 824–833, 2007.
[105] X. Yan, J. Han, and R. Afshar. CloSpan: Mining Closed Sequential Patterns in Large Datasets.
Proceedings of the 3rd SIAM International Conference on Data Mining (SDM03), pages 166–
177, May 2003.
[106] C. Yang, U. Fayyad, and P. Bradley. Efficient Discovery of Error-Tolerant Frequent Itemsets in
High Dimensions. Proceedings of the 7th ACM SIGKDD International Conference on Knowledge
Discovery in Data Mining, 2001.
[107] Q. Yang and H. H. Zhang. Web-log Mining for Predictive Web Caching. IEEE Transaction on
Knowledge and Data Engineering, 15(4):1050–1053, July 2003.
[108] Q. Yang, H. H. Zhang, and T. Li. Mining Web Logs for Prediction Models in WWW Caching
and Prefetching. Proceedings of the 7th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, pages 473–478, August 2001.
[109] S.-J. Yen. The Studies ofMining Frequent Patterns Based on Frequent Pattern Tree. Proceedings
of the 13th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD09),
2009.
[110] D. Yuan, K. Lee, H. Cheng, G. Krishna, Z. Li, X.Ma, Y. Zhou, and J. Han. CISpan: Comprehensive
Incremental Mining Algorithms of Closed Sequential Patterns for Multi-versional Software
Mining. Proceedings of the 8th SIAMInternational Conference on DataMining (SDM08), 2008.
[111] M. Zaki. Efficient enumeration of frequent sequences. Proceedings of the 7th International
Conference on Information and Knowledge Management (CIKM98), November 1998.
[112] M. Zhang, B. Kao, D.W.-L. Cheung, and C. L. Yip. Efficient Algorithms for Incremental Update
of Frequent Sequences. Proceedings of the 6th Pacific-Asia Conference on Knowledge Discovery
and Data Mining (PAKDD02), pages 186–197, 2002.
[113] Q. Zhang, J. Liu, andW.Wang. Approximate Clustering on Distributed Data Streams. Proceedings
of the 24th IEEE International Conference on Data Engineering (ICDE07), pages 1131–
1139, 2008.