改良屬性導向歸納法挖掘多值資料演算法之研究

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姓名

黃書猛(Shu-Meng Huang) 查詢紙本館藏

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企業管理學系

論文名稱

改良屬性導向歸納法挖掘多值資料演算法之研究
(A Study on the Modified Attribute Oriented Induction Algorithm of Mining the Multi Value Attribute data)

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摘要(中)

屬性導向歸納法(簡稱為AOI方法)是最重要的資料挖礦方法的其中一種，AOI 方法的輸入值包含一個關連式資料表和屬性相關的概念階層，輸出是任務相關資料所歸納之廣義特徵，雖然傳統AOI 方法用在廣義特徵的尋找非常有用，但它只能挖掘單值屬性資料的特徵，如果資料具有多值屬性，傳統的AOI方法就無法找到資料的廣義知識;另AOI 演算法須以建立概念階層為歸納依據，不同的分類原則，或不同的分類值，其所得出的概念樹即不同，影響歸納的結論，基於這個問題，本論文提出一種結合化簡布林數值的卡諾圖(Karnaugh Map)之改良式AOI 演算法，不需建立概念樹，並可以處理多值屬性的資料，找出其中各屬性間隱含的廣義特徵。

摘要(英)

Attribute Oriented Induction method （short for AOI）is one of themost important methods of data mining. The input value of AOI contains a relational data table and attribute-related concept hierarchies. The output is a general feature inducted by the related data. Though it is useful in searching for general feature with traditional AOI method, it only can mine the feature from the single-value attribute data. If the data is of multiple-value attribute, the traditional AOI method is not able to find general knowledge from the data. In addition, the AOI algorithm is based on the way of induction to establish the concept hierarchies. Different principles of classification or different category values produce different concept trees, therefore, affecting the inductive conclusion. Based on the issue, this paper proposes a modified AOI algorithm combined with a simplified Boolean bit Karnaugh map. It does not need to establish the concept tree. It can handle data of multi value and find out the general features implied within the attributes.

關鍵字(中)

★ 卡諾圖
★ 布林數值
★ 屬性導向歸納法
★ 多值屬性

關鍵字(英)

★ Attribute Oriented Induction
★ Multi-Value-Attribute
★ Boolean bit
★ Karnaugh Map

論文目次

Contents
中文摘要 ………………………………………………………………… i
Abstract…………………………………………………………………… ii
Acknowledgment………………………………………………………… iii
Contents………………………………………………………………… iv
Figures …………………………………………………………………… v
Tables…………………………………………………………………… vi
1. Introduction…………………………………………………………… 1
2.Related Works………………………………………………………… 6
3. The Algorithm of Multi-value AOI…………………………………… 7
3.1Data Structure…………………………………………………… 7
3.2 Composition of Binary Values…………………………………… 8
3.3Binary Induction with the Application of Simplification Karnaugh Map 9
3.4 Stop Condition of The Induction………………………………… 12
3.5 Inductive Rule…………………………………………………… 13
3.6 Simplification Rules of The Modified Karnaugh Map …………… 16
3.7 Proof of The Simplification Rules of The Modified Karnaugh Map 17
4. Performance Evaluation……………………………………………… 19
4.1 Experimental Environment……………………………………… 19
4.2 Experimental Results and Performance Evaluation……………… 20
5. Application…………………………………………………………… 23
5.1 Apply MAOI on the Recommendations of Library Collections…… 23
5.2 Introduction……………………………………………………… 23
5.3 Related works…………………………………………………… 24
5.4 Research method…………………………………………………… 25
5.4.1 Conceptual framework……………………………………… 25
5.4.2 Research process…………………………………………… 26
5.5 Multi-valued table………………………………………………… 26
5.5.1 Boolean bit transformation………………………………… 28
5.5.2 Karnaugh Map Concept……………………………………… 28
5.5.3 Data replacement…………………………………………… 30
5.5.4 Scan and recount…………………………………………… 30
5.6 Results……………………………………………………………… 32
6. Conclusionsand Future Works………………………………………… 33
Reference ………………………………………………………………… 35
Appendix ………………………………………………………………… 38

參考文獻

Reference
[1] Chen, M.S., Han, J., and Yu, P.S. (1996), “Data mining: An overview from a databaseperspective,”IEEE Transactions on Knowledge and Data Engineering, 8(6), 866-883.
[2] Han, J. and Kamber, M. (2001), Data Mining: Concepts and Techniques, New York: Academic Press.
[3] Cai, Y., Cercone, N., and Han, J. (1990), “An attribute-oriented approach for learning classification rules from relational databases,” in Proceedings of Sixth International Conference on Data Engineering, 281–288.
[4] Han, J., Cai, Y., and Cercone, N. (1992), “Knowledge discovery in databases: An attribute-oriented approach,” in Proceedings of International Conference on Very Large Data Bases, 547-59.
[5] Han, J., Cai, Y., and Cercone, N. (1993), “Data-driven discovery of quantitative rules in relational databases,”IEEE Transactions on Knowledge and Data Engineering, 5(1), 29-40.
[6] Han, J., Cai, Y., and Cercone, N. (1993), “Data-driven discovery of quantitative rules in relational databases,”IEEE Transactions on Knowledge and Data Engineering, 5(1), 29-40.
[7] Lu, W., Han, J., Ooi, B.C., (1993), “Discovery of general knowledge in large spatial databases,” In: Proceedings of 1993 Far East Workshop on Geographic Information Systems (FEGIS-93), 275-289.
[8] Carter, C.L. and Hamilton, H.J. (1995), “Performance evaluation of attribute-oriented algorithms for knowledge discovery from databases,” in Proceedings of Seventh International Conference on Tools with Artificial Intelligence, 486–489.
[9] Carter, C.L. and Hamilton, H.J. (1998), “Efficient attribute-oriented generalization for knowledge discovery from large databases,”IEEE Transactions on Knowledge and Data Engineering, 10(2), 193-208.
[10] Cheung, D.W., Hwang, H.Y., Fu, A.W., and Han, J. (2000), “Efficient rule-based attribute-oriented induction for data mining,”Journal of Intelligent Information Systems, 15(2), 175-200.
[11] Hamilton, H.J., Hilderman, R.J., and Cercone, N. (1996), “Attribute-oriented induction using domain generalization graphs,” in Proceedings of Eighth IEEE International Conference on Tools with Artificial Intelligence, 246-52.
[12] McClean, S., Scotney, B., Shapcott, M., (2000), “Incorporating domain knowledge into attribute-oriented data mining,” International Journal of Intelligent Systems, 15 (6), 535-548.
[13] Hamilton, H.J., Hilderman, R.J., and Cercone, N. (1996), “Attribute-oriented induction using domain generalization graphs,” in Proceedings of Eighth IEEE International Conference on Tools with Artificial Intelligence, 246-52.
[14] S. Tsumoto(2000), “Knowledge discovery in clinical databases and evaluation of discovered knowledge in outpatient clinic”, Information Sciences 124(1) 125-37.
[15] Y. L. Chen, C. C. Shen, (2005), “Mining generalized knowledge from ordered data through attribute-oriented induction techniques,”European Journal of Operational Research, 166, 221–245.
[16] C.L. Carter and H.J. Hamilton(1998), “Efficient attribute-oriented generalization for knowledge discovery from large databases,” IEEE Transactions on Knowledge and Data Engineering, 10(2) 193-208.
[17] C.-C. Hsu(2004), “Extending attribute-oriented induction algorithm for major values and numeric values,” Expert Systems with Applications , 27(2) 187-202.
[18] K.M. Lee(2001), “Mining generalized fuzzy quantitative association rules with fuzzygeneralization hierarchies,” Joint 9th IFSA World Congress and 20th NAFIPS International Conference, 2977-82.
[19] G. Raschia and N. Mouaddib, Saintetiq, (2002): A fuzzy set-based approach to database summarization, Fuzzy Sets and Systems, 129(2) 137-62.
[20] R.A. Angryk and F.E. Petry, (2005),“Mining multi-level associations with fuzzy hierarchies,” The 14th IEEE International Conference on Fuzzy Systems, FUZZ’’05. 785-90.
[21] U. Hierarchies, (1997), “Database summarization using fuzzy isa hierarchies,”IEEE Transactions on Systems, Man, and Cybernetics-Part B, 27(1) 68-78.
[22] E.M. Knorr and R.T. Ng, (1996), “Extraction of spatial proximity patterns by concept generalization,” Second International Conference on Knowledge Discovery and Data Mining, 347–50.
[23] L.Z. Wang, L.H. Zhou and T. Chen, (2004),“A new method of attribute-oriented spatial generalization,”Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 1393-8.
[24] S. Tsumoto, (2000),“Knowledge discovery in clinical databases and evaluation of discovered knowledge in outpatient clinic,”Information Sciences, 124(1) 125-37.
[25] Chen, Y.-L., Hsu, C.-L., and Chou, S.-C. (2003),“Constructing a Multi-valued and Multi-labeled Decision Tree,”Expert Systems with Applications, 25(2), 199-209.
[26] Jun-Rong Huang. (2006), “Using clusters to find the most adaptive recommendations of books” Journal of Educational Media & Library Science, 43(3), 309-325.
[27] Ou, J., Lin, S. and Li, J. (2001), “The Personalized Index Service System in Digital Library,” Proc. of the Third International Symposium on Cooperative Database Systems for Advanced Applications, 92-99.
[28] Schafer, J. B., Konstan,J. A. and Riedl,J. (2001), “ E-Commerce Recommendation Applications,” Data Mining and Knowledge Discovery, 5(1), 115-153.
[29] Ansari,A., Essengaier,S. and R. Kohli. (2000), “ Internet Recommendation Systems,” Journal of Marketing Research, 37(3), 363-375.
[30] Shu-Meng Huang. (2010), “ A study on the Modified Attributed-Oriented-Induction Algorithm of Mining the Multi-Value Attribute Data”, ICERM, 62.
[31]Lee,W.P., Liu,C.H. and Lu,C.C. (2002), “Intelligent agent- based systems for personalized recommendations in Internet commerce,” Expert Systems with Applications, 22(4), 275-284.

指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2012-1-17

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