(3.210.184.142) 您好!臺灣時間:2021/05/12 03:34
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:鄧維光
研究生(外文):Wei-Guang Teng
論文名稱:資料串流環境中之頻繁時間樣式探勘
論文名稱(外文):Mining of Frequent Temporal Patterns on Data Streams
指導教授:陳銘憲陳銘憲引用關係
指導教授(外文):Ming-Syan Chen
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:121
中文關鍵詞:資料串流小波轉換頻繁時間樣式資料探勘
外文關鍵詞:Frequent Temporal PatternData StreamData MiningWavelet Transform
相關次數:
  • 被引用被引用:0
  • 點閱點閱:156
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
近年來,許多資料查詢與資料探勘的相關議題被引進資料串流的環境中。在眾多資料探勘議題中,以購物交易資料進行頻繁樣式的探索已被公認為極具重要性之研究方向。在此論文中,主要的研究課題有三:一、以靜態的交易資料庫作為出發點來探討頻繁樣式的探勘模型,進而推廣其觀念以應用於線上環境所產生之交易資料串流;二、研究資料串流環境中有限資源之有效利用方式;三、研發追蹤線上資料串流時的品質保證機制。其明確之相關研究內容簡述如下:
由探索頻繁樣式進而推導關連性法則的流程,我們發展了替代性法則此一新的資料探勘技術。所謂的替代性是指當購買行為發生時,顧客對於商品間的選擇與取捨關係。此種替代性法則的發掘,可以有效提供關於購物預測、顧客行為分析與決策支援等各方面的寶貴知識。具體而言,藉由建立替代性法則的理論基礎,我們更歸納商品組合間的出現頻率關係,來增進計算負面商品組合時的效率,並進而獲取深具統計意義的結果。
為了在資料串流環境中探索頻繁時間樣式,我們首先發展出一適用於各種時間樣式之頻率計數架構,並設計一具有兩大主要特色的演算法:首先是僅對每筆記錄進行單次掃瞄來線上蒐集各統計量值,另一特色則是利用迴歸理論產生簡潔的樣式表示式。藉此,線上的交易資料串流可即時地轉化為各種可能的頻繁樣式,各樣式的頻率變化亦可以多線段的迴歸方法來追蹤,而更利用了區段微調 (segment tuning) 與區段緩和(segment relaxation) 的技巧來確保記憶體的使用容量。結合這些特性後,此演算法不僅能對可變的時間區間進行資料探勘工作,並能有效地進行趨勢偵測。
在資料串流環境中另一應被重視的課題為:如何妥善利用記憶體空間與運算能力等有限的資源來產生準確的預估模型。針對追蹤線上時間序列時不同的精細度考量,系統資源可確保被利用於使用者較為重視的部分,例如:時間精細度是指隨著時間的變化,人們對越新發生的事件較感興趣,這意味著較多的系統資源應被用於仔細地探索較新的資料;此外,當進行頻繁時間樣式之資料探勘工作時,較多的資源也應用於處理所謂的邊緣樣式(borderline patterns)—發生頻率非常接近臨界值者,藉此能有效地辨別正確的頻繁樣式。有鑑於此,我們發展了以小波理論為基礎的演算法,來實現此一具資源感知性的樣式探勘工作,而藉由動態調配記憶體空間的使用方式,多個動態資料串流所產生之時間樣式亦可被正確發掘。
為追蹤由感測器收集得來或由資料探勘演算法產生之時間序列,我們利用小波轉換中的能量守恆特性來推導L1 與L2 誤差的理論關係,在藉由捨去較不重要的小波係數以節省寶貴系統資源時,可提供還原原始序列後的誤差保證。此外,為了處理無限長的線上資料串流,我們提出一個較佳的資料結構以適用於動態的資料摘要保存方式。經實驗證明,此種具解析度可適性之漸進拆解法,在保留時間序列重要特徵上所花的記憶體空間十分小,而當進行漸進式資料更新時,可以得到近似最佳解。
In recent years, several query problems and mining capabilities have been explored for a data stream environment. Among various data mining capabilities, the one receiving a significant amount of research attention is on mining frequent patterns over market basket data. In this
dissertation, we first explore the model of frequent itemsets from static transaction databases and generalize relevant concepts to discovering of temporal relationship from online transaction flows. Then, we investigate the resource utilization issues in a data stream environment. Finally, we study the problem of quality guarantees when tracking online data streams.
For the problem of mining frequent itemsets to derive association rules, a new mining capability, called mining of substitution rules, is first developed by extending the concepts of mining of association rules. Substitution refers to the choice made by a customer to replace the purchase of some items with that of others. The discovery of substitution rules, same as that of association rules, will lead to very valuable knowledge in various aspects, including market prediction, user behavior analysis and decision support. Specifically, we first derive theoretical properties for the model of substitution rule mining and devise a technique on the induction of positive itemset supports to improve the efficiency of support counting for negative itemsets. Then, in light of these properties, algorithm SRM (standing for substitution rule mining) is designed and implemented to discover the substitution rules efficiently while attaining good statistical significance.
To mine frequent temporal patterns on data streams, a regression-based algorithm, called algorithm FTP-DS (Frequent Temporal Patterns of Data Streams) is devised. While providing a general framework of pattern frequency counting, algorithm FTP-DS has two major features, namely one data scan for online statistics collection and regression-based compact pattern representation. To attain the feature of one data scan, the data segmentation and the pattern growth scenarios are explored for the frequency counting purpose. Algorithm FTP-DS scans online transaction flows and generates candidate frequent patterns in real time. The second important feature of algorithm FTP-DS is on the regression-based compact pattern representation.
In addition, we develop the techniques of the segmentation tuning and segment relaxation to enhance the functions of FTP-DS. With these features, algorithm FTP-DS is able to not only conduct mining with variable time intervals but also perform trend detection effectively.
The fundamental problem that how the limited resources, e.g., memory space and computation power, can be well utilized to produce accurate estimates in a data stream environment is also addressed. Two important features for tracking mined patterns with properly utilized resources are examined. The first issue is temporal granularity which refers to the phenomenon that as time advances, people are more interested in recent events, meaning that more resources can be utilized to explore more recent data with finer granularities. Second, with the mining task of discovering frequent temporal patterns, more resources are expected to be allocated to the processing of those borderline patterns whose statistics, e.g., occurrence frequencies, are close to the specified threshold so as to have proper frequent itemset identification. This feature is
called mining with support count granularity. Consequently, a wavelet-based algorithm, called algorithm RAM-DS (Resource-Aware Mining for Data Streams) is devised to perform general pattern mining tasks for data streams by exploring both temporal and support count granularities.
Algorithm RAM-DS is designed to not only reduce the memory required for data storage but also retain good approximation of target time series. In addition, algorithm RAM-DS can support a varying number of data streams by allocating memory space adaptively when tracking
patterns generated from online transactions.
For tracking online time series data which is directly collected from sensors or is generated by stream mining algorithms, we explore the energy preservation property of wavelet-based transform. The commonly used L1- and L2-error metrics are theoretically guaranteed when insignificant coefficients are discarded for saving precious resources in our framework. In addition, to handle infinite online data flows, an enhanced data structure RAID-tree which is based on the error tree is proposed for dynamic synopses maintenance over data streams. Specifically, an algorithm RAID with the resolution adaptability for incremental decomposition is developed. Experimental results have shown that the memory required for storing significant features of time series data is very small and the quality of approximation is stable when performing incremental
data updates.
1 Introduction 7
1.1 Motivation and Overview of the Dissertation . . . . . . . . . . . . . . . . . . . . . 7
1.2 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 Extending from Association Rules to Substitution Rules 12
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Mining of Association Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 Apriori-Based Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.2 Partition-Based Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.3 Pattern Growth Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3 Model of Substitution Rule Mining . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3.1 Mining of Negative Association Rules . . . . . . . . . . . . . . . . . . . . . 23
2.3.2 Mining of Substitution Rules . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.4 SRM: Substitution Rule Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.4.1 Algorithm of Substitution Rule Mining . . . . . . . . . . . . . . . . . . . . 33
2.4.2 Overhead Reduction of Support Counting . . . . . . . . . . . . . . . . . . 35
2.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.5.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.5.2 Execution Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.5.3 Evaluation of Rule Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3 A Temporal Pattern Mining Scheme for Data Streams 44
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2.1 Types of Temporal Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2.2 Support Framework for Temporal Patterns . . . . . . . . . . . . . . . . . . 48
3.2.3 Tracking Frequency Counts overData Streams . . . . . . . . . . . . . . . . 50
3.3 Mining Temporal Patterns in a Data Stream . . . . . . . . . . . . . . . . . . . . . 51
3.3.1 Major Features of Algorithm FTP-DS . . . . . . . . . . . . . . . . . . . . 51
3.3.2 Algorithmof FTP-DS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.3.3 Enhanced Techniques for FTP-DS . . . . . . . . . . . . . . . . . . . . . . . 59
3.4 Advantages and Enhancements of Algorithm FTP-DS . . . . . . . . . . . . . . . . 60
3.4.1 Mining with Flexible Time Intervals . . . . . . . . . . . . . . . . . . . . . . 60
3.4.2 Trend Identification and Change Detection . . . . . . . . . . . . . . . . . . 61
3.4.3 Capturing and Maintenance of Dynamic Time Series . . . . . . . . . . . . 62
3.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.5.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.5.2 Feasibility and Scalability of FTP-DS . . . . . . . . . . . . . . . . . . . . . 64
3.5.3 Comparison with Batch Mode Algorithm . . . . . . . . . . . . . . . . . . . 65
3.5.4 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.5.5 Experiments on a Real Dataset . . . . . . . . . . . . . . . . . . . . . . . . 68
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4 Resource-Aware Mining for Data Streams 70
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.2.1 Issues of Processing Data Streams . . . . . . . . . . . . . . . . . . . . . . . 73
4.2.2 Support Framework for Mining Temporal Patterns . . . . . . . . . . . . . 74
4.2.3 Multiresolution Analysis of Wavelet-based Transform . . . . . . . . . . . . 75
4.2.4 Online Updates withWavelet-Based Approaches . . . . . . . . . . . . . . . 76
4.2.5 Thresholding and Energy Preservation of Wavelet Transform . . . . . . . . 78
4.3 Resource-Aware Mining with Selective Capability . . . . . . . . . . . . . . . . . . 79
4.3.1 Resource Reuse and Adaptive Resource Allocation . . . . . . . . . . . . . . 80
4.3.2 Resource-AwareMining for Data Streams . . . . . . . . . . . . . . . . . . 80
4.3.3 Flows of Algorithm RAM-DS . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.3.4 Adaptive Resource Management . . . . . . . . . . . . . . . . . . . . . . . . 85
4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.4.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.4.2 Feasibility of RAM-DS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.4.3 Incremental Maintenance of Online Data . . . . . . . . . . . . . . . . . . . 88
4.4.4 Mining of Temporal Patterns under Varying Number of Data Streams . . . 90
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5 Maintaining Synopses with Quality Guarantees for Data Streams 93
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.2.1 Dimensionality Reduction for Time Series . . . . . . . . . . . . . . . . . . 95
5.2.2 Similarities among Multiple Time Series . . . . . . . . . . . . . . . . . . . 97
5.3 Maintaining Data Stream Synopses withQuality Guarantees . . . . . . . . . . . . 98
5.3.1 Energy Preservation and Thresholding of Wavelet Transforms . . . . . . . 98
5.3.2 Incremental Maintenance of Data Stream Synopses . . . . . . . . . . . . . 101
5.3.3 Algorithm RAID (Resolution Adaptability for Incremental Decomposition) 104
5.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.4.1 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.4.2 Effectiveness of the Upper Bound for L1-Error . . . . . . . . . . . . . . . . 107
5.4.3 Comparisons of Top-K Thresholding, Algorithm SWAT and Algorithm RAID108
5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6 Conclusions 112
[1] R. C. Agarwal, C. C. Aggarwal, and V. V. V. Prasad. A Tree Projection Algorithm for
Generation of Frequent Itemsets. Journal of Parallel and Distributed Computing (Special
Issue on High Performance Data Mining), 61(3):350—371, March 2001.
[2] C. C. Aggarwal and P. S. Yu. A New Framework for Itemset Generation. Proceedings of
the 17th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems,
pages 18—24, June 1998.
[3] R. Agrawal, T. Imielinski, and A. Swami. Mining Association Rules between Sets of Items
in Large Databases. Proceedings of the 1993 ACM SIGMOD International Conference on
Management of Data, pages 207—216, May 1993.
[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, pages
478—499, September 1994.
[5] R. Agrawal and R. Srikant. Mining Sequential Patterns. Proceedings of the 11th International
Conference on Data Engineering, pages 3—14, March 1995.
[6] J. M. Ale and G. Rossi. An Approach to Discovering Temporal Association Rules. Proceedings
of the 2000 ACM Symposium on Applied Computing, pages 294—300, March 2000.
[7] 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, April 2001.
[8] R. J. Bayardo, R. Agrawal, and D. Gunopulos. Constraint-Based Rule Mining in Large,
Dense Databases. Proceedings of the 15th International Conference on Data Engineering,
pages 188—197, March 1999.
[9] J.-F. Boulicaut, A. Bykowski, and B. Jeudy. Towards the Tractable Discovery of Association
Rules with Negations. Proceedings of the 4th International Conference on Flexible Query
Answering Systems, pages 425—434, October 2000.
[10] S. Brin, R. Motwani, and C. Silverstein. Beyond Market Baskets: Generalizing Association
Rules to Correlations. Proceedings of the 1997 ACM SIGMOD International Conference on
the Management of Data, pages 265—276, May 1997.
[11] S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic Itemset Counting and Implication
Rules for Market Basket Data. Proceedings of the 1997 ACM SIGMOD International
Conference on Management of Data, pages 255—264, May 1997.
[12] A. Bulut and A. K. Singh. SWAT: Hierarchical Stream Summarization in Large Networks.
Proceedings of the 19th International Conference on Data Engineering, pages 303—
314, March 2003.
[13] C. S. Burrus, R. A. Gopinath, and H. Guo. Introduction to Wavelets and Wavelet Transforms:
A Primer. Prentice-Hall, Inc., 1998.
[14] S. Chakrabarti, S. Sarawagi, and B. Dom. Mining Surprising Patterns Using Temporal
Description Length. Proceedings of the 24th International Conference on Very Large Data
Bases, pages 606—617, August 1998.
[15] K.-P. Chan and A. W.-C. Fu. Ecient Time Series Matching by Wavelets. Proceedings of
the 15th International Conference on Data Engineering, pages 126—133, March 1999.
[16] S. Chandrasekaran and M. J. Franklin. Streaming Queries over Streaming Data. Proceedings
of the 28th International Conference on Very Large Data Bases, pages 203—214, August 2002.
[17] J. H. Chang and W. S. Lee. estWin: Adaptively Monitoring the Recent Change of Frequent
Itemsets over Online Data Streams. Proceedings of the 2003 ACM CIKM International
Conference on Information and Knowledge Management, pages 536—539, November 2003.
[18] J. H. Chang and W. S. Lee. Finding Recent Frequent Itemsets Adaptively over Online Data
Streams. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, pages 487—492, August 2003.
[19] M.-S. Chen, J. Han, and P. S. Yu. Data Mining: An Overview from Database Perspective.
IEEE Transactions on Knowledge and Data Engineering, 8(6):866—883, December 1996.
[20] M.-S. Chen, J.-S. Park, and P. S. Yu. Ecient Data Mining for Path Traversal Patterns.
IEEE Transactions on Knowledge and Data Engineering, 10(2):209—221, April 1998.
[21] X. Chen and I. Petrounias. Discovering Temporal Association Rules: Algorithms, Language
and System. Proceedings of the 16th International Conference on Data Engineering, page
306, February 2000.
[22] Y. Chen, G. Dong, J. Han, B.W.Wah, and J.Wang. Multi-Dimensional Regression Analysis
of Time-Series Data Streams. Proceedings of the 28th International Conference on Very
Large Data Bases, pages 323—334, August 2002.
[23] D. Cheung, J. Han, V. Ng, and C. Y.Wong. Maintenance of Discovered Association Rules in
Large Databases: An Incremental Updating Technique. Proceedings of the 12th International
Conference on Data Engineering, pages 106—114, February 1996.
[24] D. Cheung, S. D. Lee, and B. Kao. A General Incremental Technique for Updating Discovered
Association Rules. Proceedings of the Fifth International Conference On Database
Systems for Advanced Applications, pages 185—194, April 1997.
[25] E. Cohen, M. Datar, S. Fujiwara, A. Gionis, P. Indyk, R. Motwani, J. D. Ullman, and
C. Yang. Finding Interesting Associations without Support Pruning. IEEE Transactions
on Knowledge and Data Engineering, 13(1):64—78, January 2001.
[26] G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing Data Streams Using
Hamming Norms (How to Zero In). IEEE Transactions on Knowledge and Data Engineering,
15(3):529—540, May 2003.
[27] G. Das, K.-I. Lin, H. Mannila, G. Renganathan, and P. Smyth. Rule Discovery from Time
Series. Proceedings of the 4th ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining, pages 16—22, August 1998.
[28] M. Datar, A. Gionis, P. Indyk, and R. Motwani. Maintaining Stream Statistics over Sliding
Windows. Proceedings of the 2002 Annual ACM-SIAM Symposium on Discrete Algorithms,
pages 635—644, January 2002.
[29] A. Dobra, M. N. Garofalakis, J. Gehrke, and R. Rastogi. Processing Complex Aggregate
Queries over Data Streams. Proceedings of the 2002 ACM SIGMOD International Conference
on Management of Data, pages 61—72, June 2002.
[30] P. Domingos and G. Hulten. Mining High-Speed Data Streams. Proceedings of the 6th ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 71—80,
August 2000.
[31] D. L. Donoho. De-noising by Soft-Thresholding. IEEE Transactions on Information Theory,
41(3):613—627, May 1995.
[32] W. DuMouchel and D. Pregibon. Empirical Bayes Screening for Multi-Item Associations.
Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining, pages 67—76, August 2001.
[33] V. Ganti, J. Gehrke, and R. Ramakrishnan. Mining Data Streams under Block Evolution.
SIGKDD Explorations, 3(2):1—10, January 2002.
[34] M. N. Garofalakis, J. Gehrke, and R. Rastogi. Querying and Mining Data Streams: You
Only Get One Look. Proceedings of the 2002 ACM SIGMOD International Conference on
Management of Data, June 2002.
[35] J. Gehrke, F. Korn, and D. Srivastava. On Computing Correlated Aggregates Over Continual
Data Streams. Proceedings of the 2001 ACM SIGMOD International Conference on
Management of Data, pages 13—24, May 2001.
[36] A. C. Gilbert, Y. Kotidis, S. Muthukrishnan, and M. Strauss. Surfing Wavelets on Streams:
One-Pass Summaries for Approximate Aggregate Queries. Proceedings of the 27th International
Conference on Very Large Data Bases, pages 79—88, September 2001.
[37] S. Guha, D. Gunopulos, and N. Koudas. Correlating Synchronous and Asynchronous Data
Streams. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, pages 529—534, August 2003.
[38] S. Guha, N. Mishra, R. Motwani, and L. O’Callaghan. Clustering Data Streams. Proceedings
of the 41st Annual Symposium on Foundations of Computer Science, pages 359—366,
November 2000.
[39] J. Han, G. Dong, and Y. Yin. Ecient Mining of Partial Periodic Patterns in Time Series
Database. Proceeding of the 15th International Conference on Data Engineering, pages
106—115, March 1999.
[40] J. Han and Y. Fu. Discovery of Multiple-Level Association Rules from Large Databases.
Proceedings of the 21th International Conference on Very Large Data Bases, pages 420—431,
September 1995.
[41] J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann
Publishers, August 2000.
[42] J. Han, L. V. S. Lakshmanan, and R. T. Ng. Constraint-Based, Multidimensional Data
Mining. COMPUTER (Special Issue on Data Mining), pages 46—50, 1999.
[43] 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.
[44] J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation.
Proceedings of the 2000 ACM-SIGMOD International Conference on Management of Data,
pages 1—12, May 2000.
[45] R. V. Hogg and E. A. Tanis. Probability and Statistical Inference, 6/e. Prentice-Hall International,
Inc., December 2000.
[46] J. C. Hosseini, R. R. Harmon, and M. Zwick. An Information Theoretic Framework for
Exploratory Multivariate Market Segmentation Research. Decision Sciences, 22:663—677,
1991.
[47] G. Hulten, L. Spencer, and P. Domingos. Mining Time-Changing Data Streams. Proceedings
of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data
Mining, pages 97—106, August 2001.
[48] P. Indyk, N. Koudas, and S. Muthukrishnan. Identifying Representative Trends in Massive
Time Series Data Sets Using Sketches. Proceedings of the 26th International Conference on
Very Large Data Bases, pages 363—372, September 2000.
[49] C. Jermaine. The Computational Complexity of High-Dimensional Correlation Search. Proceedings
of the 1st IEEE International Conference on Data Mining, pages 249—256, November
2001.
[50] R. A. Johnson and D. W. Wichern. Applied Multivariate Statistical Analysis, 5/e. Prentice-
Hall International, Inc., November 2001.
[51] E. Keogh, K. Chakrabarti, S. Mehrotra, and M. Pazzani. Locally Adaptive Dimensionality
Reduction for Indexing Large Time Series Databases. Proceedings of the 2001 ACM
SIGMOD International Conference on Management of Data, pages 151—162, May 2001.
[52] E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra. Dimensionality Reduction for Fast
Similarity Search in Large Time Series Databases. Knowledge and Information Systems,
3(3):263—286, August 2001.
[53] E. J. Keogh, S. Chu, D. Hart, and M. J. Pazzani. An Online Algorithm for Segmenting
Time Series. Proceedings of the 1st IEEE International Conference on Data Mining, pages
289—296, November 2001.
[54] J. Kleinberg, R. Motwani, P. Raghavan, and S. Venkatasubramanian. Storage Management
for Evolving Databases. Proceedings of the 38th IEEE Annual Symposium on Foundations
of Computer Science, pages 353—362, October 1997.
[55] L. V. S. Lakshmanan, R. Ng, J. Han, and A. Pang. Optimization of Constrained Frequent Set
Queries with 2-Variable Constraints. Proceedings of the 1999 ACM SIGMOD International
Conference on Management of Data, pages 157—168, June 1999.
[56] I. Lazaridis and S.Mehrotra. Capturing Sensor-Generated Time Series with Quality Guarantees.
Proceedings of the 19th International Conference on Data Engineering, pages 429—440,
March 2003.
[57] 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, pages 337—344, November 2001.
[58] C.-H. Lee, C.-R. Lin, and M.-S. Chen. Sliding-Window Filtering: An Ecient Algorithm
for Incremental Mining. Proceedings of ACM 10th International Conference on Information
and Knowledge Management, pages 263—270, November 2001.
[59] C.-H. Lee, P. S. Yu, and M.-S. Chen. Causality Rules: Exploring the Relationship between
Triggering and Consequential Events in a Database of Short Transactions. Proceedings of
the 2nd SIAM International Conference on Data Mining, April 2002.
[60] J. L. Lin and M. H. Dunham. Mining Association Rules: Anti-Skew Algorithms. Proceedings
of the 14th International Conference on Data Engineering, pages 486—493, February 1998.
[61] 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, pages 337—341, August 1999.
[62] B. Liu, W. Hsu, and Y. Ma. Identifying Non-Actionable Association Rules. Proceedings of
the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,
pages 329—334, August 2001.
[63] H. Lu, J. Han, and L. Feng. Stock Movement Prediction and N-Dimensional Inter-
Transaction Association Rules. Proceedings of the 1998 ACM SIGMOD Workshop on Research
Issues on Data Mining and Knowledge Discovery, pages 12:1—12:7, June 1998.
[64] S. Ma and J. L. Hellerstein. Mining Mutually Dependent Patterns. Proceedings of the 1st
IEEE International Conference on Data Mining, pages 409—416, November 2001.
[65] G. S. Manku and R. Motwani. Approximate Frequency Counts over Streaming Data. Proceedings
of the 28th International Conference on Very Large Data Bases, pages 346—357,
August 2002.
[66] H. Mannila and D. Rusakov. Decomposition of Event Sequences into Independent Components.
Proceedings of the 1st SIAM Conference on Data Mining, April 2001.
[67] H. Mannila, H. Toivonen, and A. I. Verkamo. Ecient Algorithms for Discovering Association
Rules. Proceedings of AAAI Workshop on Knowledge Discovery in Databases, pages
181—192, July 1994.
[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] Y. Matias, J. C. Vitter, andM.Wang. Wavelet-Based Histograms for Selectivity Estimation.
Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data,
pages 448—459, June 1998.
[70] R. Meo. Theory of Dependence Values. ACM Transactions on Database Systems, 25(3):380—
406, September 2000.
[71] M. Misiti, Y. Misiti, G. Oppenheim, and J.-M. Poggi. Decomposition en ondelettes et
methodes comparatives: etude d’une courbe de charge electrique. Revue de Statistique
Appliquee, 17(2):57—77, 1994.
[72] R. Motwani, J. Widom, A. Arasu, B. Babcock, S. Babu, M. Datar, G. Manku, C. Olston,
J. Rosenstein, and R. Varma. Query Processing, Resource Management, and Approximation
in a Data Stream Management System. Proceedings of the 2003 Conference on Innovative
Data Systems Research, January 2003.
[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] R. T. Ng and J. Han. Ecient and Eective Clustering Methods for Spatial Data Mining.
Proceedings of the 20th International Conference on Very Large Data Bases, pages 144—155,
September 1994.
[75] L. O’Callaghan, A. Meyerson, R. Motwani, N. Mishra, and S. Guha. Streaming-Data Algorithms
for High-Quality Clustering. Proceedings of the 18th International Conference on
Data Engineering, pages 685—696, February 2002.
[76] J.-S. Park, M.-S. Chen, and P. S. Yu. An Eective Hash-Based Algorithm for Mining Association
Rules. Proceedings of the ACM-SIGMOD International Conference on Management
of Data, pages 175—186, May 1995.
[77] J.-S. Park, M.-S. Chen, and P. S. Yu. Using a Hash-Based Method with Transaction Trimming
forMining Association Rules. IEEE Transactions on Knowledge and Data Engineering,
9(5):813—825, October 1997.
[78] 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, pages 350—354, August 2000.
[79] J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M. C. Hsu. PrefixSpan:
Mining Sequential Patterns Eciently by Prefix-Projected Pattern Growth. Proceedings of
the 17th International Conference on Data Engineering, pages 215—224, April 2001.
[80] I. Popivanov and R. J. Miller. Similarity Search over Time-Series Data UsingWavelets. Proceedings
of the 18th International Conference on Data Engineering, pages 212—221, February
2002.
[81] A. Savasere, E. Omiecinski, and S. Navathe. An Ecient Algorithm for Mining Association
Rules in Large Databases. Proceedings of the 21th International Conference on Very Large
Data Bases, pages 432—444, September 1995.
[82] A. Savasere, E. Omiecinski, and S. Navathe. Mining for Strong Negative Associations in a
Large Database of Customer Transactions. Proceeding of the 14th International Conference
on Data Engineering, pages 494—502, February 1998.
[83] R. Srikant and R. Agrawal. Mining Generalized Association Rules. Proceedings of the 21th
International Conference on Very Large Data Bases, pages 407—419, September 1995.
[84] W.-G. Teng, M.-S. Chen, and P. S. Yu. A Regression-Based Temporal Pattern Mining
Scheme for Data Streams. Proceedings of the 29th International Conference on Very Large
Data Bases, pages 93—104, September 2003.
[85] W.-G. Teng, M.-S. Chen, and P. S. Yu. Resource-Aware Mining with Variable Granularities
in Data Streams. Proceedings of the 4th SIAM International Conference on Data Mining,
April 2004.
[86] W.-G. Teng, M.-J. Hsieh, and M.-S. Chen. On the Mining of Substitution Rules for Statistically
Dependent Items. Proceedings of the IEEE 2nd International Conference on Data
Mining, pages 442—449, December 2002.
[87] H. Toivonen. Sampling Large Databases for Association Rules. Proceedings of the 22th
International Conference on Very Large Data Bases, pages 134—145, September 1996.
[88] University of Dayton and U.S. Environmental Protection Agency. Average Daily Temperature
Archive. http://www.engr.udayton.edu/weather/.
[89] J. C. Vitter and M. Wang. Approximate Computation of Multidimensional Aggregates of
Sparse Data Using Wavelets. Proceedings of the 1999 ACM SIGMOD International Conference
on Management of Data, pages 193—204, June 1999.
[90] K.Wang, Y. He, and J. Han. Mining Frequent Itemsets Using Support Constraints. Proceedings
of the 26th International Conference on Very Large Data Bases, pages 43—52, September
2000.
[91] K. Wang, S. Q. Zhou, and S. C. Liew. Building Hierarchical Classifiers Using Class Proximity.
Proceedings of the 25th International Conference on Very Large Data Bases, pages
363—374, September 1999.
[92] Y.-L.Wu, D. Agrawal, and A. E. Abbadi. A Comparison of DFT and DWT based Similarity
Search in Time-Series Databases. Proceedings of the 9th ACM International Conference on
Information and Knowledge Management, pages 488—495, November 2000.
[93] C. Yang, U. Fayyad, and P. Bradley. Ecient Discovery of Error-Tolerant Frequent Itemsets
in High Dimensions. Proceedings of the 7th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, pages 194—203, August 2001.
[94] J. Yang, W. Wang, P. S. Yu, and J. Han. Mining Long Sequential Patterns in a Noisy Environment.
Proceedings of the 2002 ACM SIGMOD International Conference on Management
of Data, pages 406—417, June 2002.
[95] B.-K. Yi and C. Faloutsos. Fast Time Sequence Indexing for Arbitrary Lp Norms. Proceedings
of the 26th International Conference on Very Large Data Bases, pages 385—394,
September 2000.
[96] B.-K. Yi, N. D. Sidiropoulos, T. Johnson, H. V. Jagadish, C. Faloutsos, and A. Biliris.
Online Data Mining for Co-Evolving Time Sequences. Proceedings of the 16th International
Conference on Data Engineering, pages 13—22, February 2000.
[97] M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. New Algorithms for Fast Discovery
of Association Rules. Proceedings of the 3rd ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, pages 283—286, August 1997.
[98] D. Zhang, D. Gunopulos, V. J. Tsotras, and B. Seeger. Temporal Aggregation over Data
Streams Using Multiple Granularities. Proceedings of the 8th International Conference on
Extending Database Technology, pages 646—663, March 2002.
[99] Y. Zhu and D. Shasha. StatStream: Statistical Monitoring of Thousands of Data Streams
in Real Time. Proceedings of the 28th International Conference on Very Large Data Bases,
pages 358—369, August 2002.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
1. [9] 沈義訓、梁朝雲,2000,網路虛擬實境博物館之互動展示設計研究,教育資料與圖書館學,37(3),pp.275-298。
2. [20] 孫春望,1998,童話幻想曲—合作式電腦遊戲設計,教學科技與媒體,37,pp.2-9。
3. [17] 林東清、楊宗珂,1993,資訊系統整合效益影響因素之多層次模式分析交大管理學報(TSSCI),13(2),pp.17-41。
4. [16] 林奇賢,1997,全球資訊網輔助學習系統: 網際網路與國小教育,資訊與教育,58,pp.2-11。
5. 23.葉凱莉,喬友慶,從管理機會方格看顧客滿意度-以百貨公司為例,企銀季刊,第23卷,第4期,pp.67-86。
6. [21] 周明,2002,知識管理導向的電子化博物館經營策略—國立自然科學博物館實徵分析,博物館學季刊,16(3),pp.67-75。
7. [12] 吳明隆,1998,以網路為主的教學環境(Web-Base Instruction)內涵及規劃原則,教育部電子計算機中心簡訊,8712,pp. 22-38。
8. 20.楊錦洲,1993,「服務品質的探討」,品質管制月刊。
9. 6.林能中,2001,「漫談標準與檢驗」,標準與檢驗雜誌,第34期, 10月,pp.1-41。
10. [29] 陳雪華、張玉欣,1997,台大電子圖書館與博物館數位化資源之組織,圖書館學與資訊科學,23(2),pp.51-65。
11. [30] 陳年興、王敏煌,1997,全球資訊網上教材製作輔助工具之研發,隔空教育論叢刊,9,pp.77-104。
12. [31] 陳年興,1998,全球資訊網整合式學習環境,資訊與教育,64,pp.2-13。
13. [32] 陳惠美,1993,電腦在國立自然科學博物館的應用,社教雙月刊。
14. [33] 陳怡靜、計惠卿,1997,育樂式課程軟體之遊戲式學習情境,視聽教育雙月刊,39 (1),pp.24-33。
15. [34] 陳百薰、項潔、姜宗模、洪政欣,2002,數位博物館探討,博物館學季刊,16(3),pp.15-38。
 
系統版面圖檔 系統版面圖檔