臺灣博碩士論文加值系統

時間序列探勘(Sequential Pattern Mining)在日常生活以及研究領域都是應用廣泛的主題之一。他的應用包含顧客消費行為分析、分析DNA 序列以找出其中隱含的規律等等。但隨著大數據時代之資料型態越來越多變，我們也需要更有效率的演算法以及探勘模模型去應對他們。這篇研究使用了帶有強度的區間時間序列，用以更好地反應真實世界具不同時間及強度之資料。本研究提出一個三階段之模型（資料轉換、序列相似度計算、分類），並使用台灣半導體產業之股票資訊作為實驗資料，以反映此三階段模型之應用。

Sequential Pattern Mining (SPM) has been widely used in our daily lives and research fields. From companies deciding their sales strategies based on customers’ buying patterns to scientists identifying hidden messages behind DNA sequences. But as data nowadays become more and more informative, the traditional SPM can no longer fully use them. This research is based on a data type called Interval-Based Temporal Sequences with Intensity (ITSI), which can better reflect the real-world data with different times and magnitudes. We propose a three-phase model: (1) the frequent ITSIs are generated, (2) similarities among ITSIs are computed, and (3) classification is applied to discover cause-and-effect relationships in a supply chain.

To demonstrate the practicability and feasibility of the three-phased knowledge-discovery process, we use Taiwan’s semiconductor industry’s stock data as the real-world database to implement the model.

TABLE OF CONTENTS
謝　詞 I
國立臺北大學110學年度第2學期碩士學位論文提要 II
ABSTRACT III
TABLE OF CONTENTS IV
LIST OF FIGURES VI
LIST OF TABLES VIII
1. INTRODUCTION 1
2. LITERATURE REVIEW 5
2.1. Temporal Pattern Mining 5
2.2. Computing the Similarity of ITSs 7
3. MODEL INTRODUCTION AND DEFINITIONS 9
3.1. KDD-ITSI Model 9
3.2. Definitions and Concepts 10
4. EXPERIMENT DESIGN 21
4.1. Data Acquisition and Transformation 21
4.2. Mining Frequent Patterns 25
4.3. Similarity Calculation and Classification 28
5. RESULTS ANALYSIS 31
5.1. Frequent Patterns Analysis 31
5.2. Classification Analysis 34
5.3. Results Conclusion 43
6. CONCLUSION 45
6.1. Research Contributions 45
6.2. Research Limitations 45
REFERENCES 49
著作權聲明 53

1. English References
Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of The 1993 ACM SIGMOD International Conference on Management of Data, Washington D.C. USA, 207-216.
Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11), 832-843.
Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J. (2018). An Introduction to Management Science: Quantitative Approach: Cengage Learning.
Bohlen, M. H., Busatto, R., & Jensen, C. S. (1998). Point-versus interval-based temporal data models. Proceedings of The 14th International Conference on Data Engineering, Orlando, FL, USA, 192-200.
Chen, Y.-L., Chiang, M.-C., & Ko, M.-T. (2003). Discovering time-interval sequential patterns in sequence databases. Expert Systems with Applications, 25(3), 343-354.
Chen, Y.-L., & Huang, T.-K. (2005). Discovering fuzzy time-interval sequential patterns in sequence databases. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 35(5), 959-972.
Chen, Y.-L., & Wu, S.-Y. (2006). Mining temporal patterns from sequence database of interval-based events. Proceedings of The International Conference on Fuzzy Systems and Knowledge Discovery, Xi’an, China, 586-595.
Faloutsos, C., Ranganathan, M., & Manolopoulos, Y. (1994). Fast subsequence matching in time-series databases. ACM SIGMOD Record, 23(2), 419-429.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37-37.
Gusfield, D. (1997). Algorithms on stings, trees, and sequences: Computer science and computational biology. ACM SIGACT News, 28(4), 41-60.
Hu, Y.-H., Huang, T. C.-K., Yang, H.-R., & Chen, Y.-L. (2009). On mining multi-time-interval sequential patterns. Data & Knowledge Engineering, 68(10), 1112-1127.
Huang, C.-K., Yang, P.-T., & Hsieh, K.-Y. (2018). Knowledge discovery of consensus and conflict interval-based temporal patterns: A novel group decision approach. Knowledge-Based Systems, 140, 201-213.
Kam, P.-S., & Fu, A. W.-C. (2000). Discovering temporal patterns for interval-based events. Proceedings of The International Conference on Data Warehousing and Knowledge discovery, Berlin, Heidelberg, 317-326.
Kostakis, O., & Papapetrou, P. (2017). On searching and indexing sequences of temporal intervals. Data mining and Knowledge Discovery, 31(3), 809-850.
Kostakis, O., Papapetrou, P., & Hollmén, J. (2011). Artemis: Assessing the similarity of event-interval sequences. Proceedings of The Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Athens, Greece, 229-244.
Lee, H. L., Padmanabhan, V., & Whang, S. (1997). The bullwhip effect in supply chains. Sloan Management Review, 38, 93-102.
Li, W., & Godzik, A. (2006). Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 22(13), 1658-1659.
Papapetrou, P., Kollios, G., Sclaroff, S., & Gunopulos, D. (2009). Mining frequent arrangements of temporal intervals. Knowledge and Information Systems, 21(2), 133-171.
Srikant, R., & Agrawal, R. (1996). Mining sequential patterns: Generalizations and performance improvements. Proceedings of The International Conference on Extending Database Technology, Avignon, France, 1-17.
Tan, P. N., Steinbach, M., Kumar, V., & Karpatne, A. (2019). Introduction to Data Mining: Pearson Education.
Winarko, E., & Roddick, J. F. (2007). ARMADA – An algorithm for discovering richer relative temporal association rules from interval-based data. Data & Knowledge Engineering, 63(1), 76-90.
Wu, S.-Y., & Chen, Y.-L. (2007). Mining nonambiguous temporal patterns for interval-based events. IEEE Transactions on Knowledge and Data Engineering, 19(6), 742-758.
Xing, Z., Pei, J., & Keogh, E. (2010). A brief survey on sequence classification. ACM SIGKDD Explorations Newsletter, 12(1), 40-48.
Xu, R., & Wunsch, D. (2005). Survey of clustering algorithms. IEEE Transactions on Neural Networks, 16(3), 645-678.
2. Internet Reference
Statementdog. (2022, March 15). 半導體產業介紹、台股上下游類股和半導體公司股價漲跌幅. Retrieved from https://statementdog.com/taiex/19-semiconductor-industry