# 臺灣博碩士論文加值系統

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 在本論文中，我們提出一個新的自動分群演算法及利用多迴歸分析技術以在關聯式資料庫系統中估計空值。首先，我們針對數值資料提出一自動分群演算法．本論文所提之自動分群演算法不須事先定義群數及也不須事先將資料予以排序，使得分群時能更有彈性。依據本論文所提出之自動分群演算法所做的分群結果來估計空值，其僅須針對其中一個分群的資料做估計空值的工作，無須針對整個資料庫系統內所有的資料做處理，本論文所提之在關聯式資料庫系統中估計空值的方法比目前已存在的方法具有更高的平均估計準確率。
 In this thesis, we present a new method for estimating null values in relational database systems using automatic clustering and multiple regression techniques. First, we present a new automatic clustering algorithm for clustering numerical data. The proposed automatic clustering algorithm does not need to determine the number of clusters in advance and does not need to sort the data in the database in advance. Then, based on the proposed automatic clustering algorithm and multiple regression techniques, we present a new method to estimate null values in relational database systems. The proposed method for estimating null values in relational database systems only needs to process a particular cluster instead of the whole database. It gets a higher average estimation accuracy rate than the existing methods for estimating null values in relational database systems.
 Chapter 1 Introduction 11.1 Motivation 11.2 Related Literature 21.3 Organization of This Thesis 2Chapter 2 Fuzzy Set Theory 42.1 Basic Concept of Fuzzy sets 42.2 Types of Membership Functions 42.3 Summary 7Chapter 3 A Method for Estimating Null Values in Relational Database Systems 83.1 Chen-and-Yeh’s Automatic Clustering Algorithm [12] 83.2 Hsiao-and-Chen’s Method to Estimated Null Values in Relational Database Systems [18] 93.2.1 An Automatic Clustering Algorithm [18] 93.2.2 A Review of Chen-and-Hsiao’s Method for Estimating Null Values in Relational Database Systems [17] 153.2.3 A Review of Chen-and-Chen’s Method for Estimating Null Values in Relational Database Systems [9] 173.3 Summary 20Chapter 4 A New Method for Estimating Null Values in Relational DatabaseSystems Using Automatic Clustering and Multiple Regression Techniques 224.1 A New Automatic Clustering Algorithm 224.2 A New Method to Estimate Null Values in Relational Database Systems 384.3 An Example of Estimating Null Value in Relation Database Systems 434.4 Summary 50Chapter 5 Estimating Null Values in Relational Database Systems withNegative Dependency Relationships between Attributes 525.1An Example of Estimating Null Values in Relational Database Systemswith Negative Dependency Relationships between Attributes 525.2A New Method to Estimate Null Values in Relational Database Systemswith Negative Dependency Relationships between Attributes 655.3 Summary 86Chapter 6 Conclusions 876.1 Contributions of This Thesis 876.2 Future Research 87References 89
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W. Lee, “A new method to generate fuzzy rules from relational database systems for estimating null values,” Cybernetics and Systems, vol. 34, no. 1, pp. 33-57, 2003.[12]S. M. Chen and M. S. Yeh, “Generation fuzzy rules from relational database systems for estimating null values,” Cybernetics and Systems, vol. 29, no. 6, pp. 363-376, 1998.[13]S. M. Chen and W. T. Jong, “Fuzzy query translation for relational database systems,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 27, no. 4, pp. 714-721,1997.[14]C. H. Cheng and J. W. Wang, “A new approach for estimating null values in relational database,” Soft Computing, vol. 10, no. 2, pp. 104-114, 2006.[15]J. H. Chiang, “Support vector learning mechanism for fuzzy rule-based modeling: A new approach,” IEEE Transactions on Fuzzy Systems, vol. 12, no. 1, pp. 1-12, 2004.[16]K. Honda and H. Ichihashi, “Linear fuzzy clustering techniques with missing values and their application to local principal component analysis,” IEEE Transactions on Fuzzy Systems, vol. 12, no. 2, pp. 183-193, 2004.[17]S. M. Chen and H. R. Hsiao, “A new method to estimate null values in relationaldatabase systems based on automatic clustering techniques,” InformationSciences, vol. 69, no. 1-2, pp. 47-69, 2005.[18]H. R. Hsiao and S. M. Chen, “A new automatic clustering algorithm for fuzzy query processing,” Proceedings of the 6th Conference on Artificial Intelligence and Applications, Kaohsiung, Taiwan, Republic of China, pp. 550-555, 2001.[19]C. M. Huang and S. M. Chen, “Estimating null values in relational database systems with a negative dependency relationship between attributes,” Proceedings of the 13th International Conference on Information Management, Taipei, Taiwan, Republic of China, vol. 1, pp. 151-158, 2002.[20]C. M. Huang and S. M. Chen, “A new method to estimate null values in relational database systems using genetic algorithms,” Proceedings of the Six Conference on Artificial Intelligence and Applications, Kauhsiung, Taiwan, Republic of China, pp. 599-604, 2001.[21]A. H. Kvanli, C. S. Guynes, and R. J. Pavur, Introduction to Business Statistics. West Publishing Company, 1986.[22]S. W. Lee and S. M. Chen, “A new method for estimating null values in relational database systems based on genetic algorithms,” Proceedings of the Seventh Conference on Artificial Intelligence and Applications, Taichung, Taiwan, Republic of China, pp. 447-452, 2002.[23]J. M. Leski, “Generalized weighted conditional fuzzy clustering,” IEEE Transactions on Fuzzy Systems, vol. 11, no. 6, pp. 709-715, 2003.[24]Y. S. Lin and S. M. Chen, “Using automatic clustering techniques for fuzzy query processing in relational database systems,” Proceedings of the 11th National Conference on Information Management, Kaohsiung, Taiwan, Republic of China, 2000.[25]S. Rıdvan, T. Kemal and A. Novruz, “A fuzzy clustering approach for findingsimilar documents using a novel similarity measure,” Expert Systems with Applications, vol. 1, no. 3, pp. 600-605.[26]S. M. Tseng, K. H. Wang, and C. I. Lee, “A preprocessing method to deal with missing values by integrating clustering and regression techniques,” Applied Artificial Intelligence, vol. 17, no. 5, pp. 535-544, 2003.[27]H. Wang, and P. M. Bell, “Fuzzy clustering analysis and multifactorial evaluation for students’ imaginative power in physics problem solving,” Fuzzy Sets and Systems, vol. 78, no. 1, pp. 95-105, 1996.[28]S. L. Wang and Y. J. Tsai, “Null queries with interval-valued ambiguous attributes,” Proceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics, San Diego, USA, vol. 3, pp. 2150-2153, 1998.[29]M. S. Yang, P. Y. Hwang, and D. H. Chen, “Fuzzy clustering algorithms for mixed feature variables,” Fuzzy Sets and Systems, vol. 141, no. 2, pp. 301-317, 2004.[30]M. S. Yeh and S. M. Chen, “An algorithm for generating fuzzy rules from relational database systems,” Proceedings of the 6th International Conference on Information Management, Taipei, Taiwan, Republic of China, pp. 219-226, 1995.[31]M. S. Yeh and S. M. Chen, “A new method for fuzzy query processing using automatic clustering techniques,” Journal of Computers, vol. 6, no. 1, pp. 1-10, 1994.[32]M. S. Yeh, S. M. Chen, and P. Y. Hsiao, “A comparison of measures of similarity of fuzzy values,” Proceedings of the 1994 Second National Conference on Fuzzy Theory and Applications, Taipei, Taiwan, Republic of China, pp. 152-159, 1994[33]L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, pp. 338-353, 1965.
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