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研究生:陳利銓
論文名稱:以約略集合方法進行知識擷取
論文名稱(外文):A Rough Set Approach Toward Knowledge Acquisition
指導教授:李選士李選士引用關係
學位類別:碩士
校院名稱:國立海洋大學
系所名稱:航運管理學系
學門:運輸服務學門
學類:運輸管理學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:72
中文關鍵詞:約略集合知識擷取資料挖掘
外文關鍵詞:rough set theorydata mining
相關次數:
  • 被引用被引用:25
  • 點閱點閱:570
  • 評分評分:
  • 下載下載:89
  • 收藏至我的研究室書目清單書目收藏:3
在我們身邊周圍環繞的許多資訊都是不明確、不完整或不確定的。現今有越來越多的資訊和資料被儲存在資料庫中,企業經營者只能依賴他們自我本身的經驗或是專家的意見以快速地作出決定。企業經營者相當困惑如何去分析這些資料與建立挖掘資訊所需的工具。所以何種資訊技術可以幫助探尋隱藏於資料庫中的資訊是現在相當重要的課題。
最近,約略集合理論被廣泛地應用在知識擷取上。約略集合理論,是由Pawlak教授於1982年[36]所提出的,這個理論對於應用在含糊和不確定為特性的環境來說是一個相當新的數學工具。雖然約略集合理論在知識擷取上相當成功,但是處理的資料型態幾乎都是非數值型態的資料。然而在真實世界中有許許多多的資料或資訊都是由數值所構成的。
在本篇論文中,我們研究如何讓約略集合理論能夠處理數值型態的資料。回顧過去相關的資料和工作。克服先前的缺點,對於約略集合處理數值資料的方式將提出一個新的演算過程。
More information on the surrounding world is imprecise, incomplete or uncertain. there are more and more information and data stored in database, Enterprise managers rely on their experiences or the domain expert’s opinions to make quick decisions. Managers may be confused with how to analyze those data and in great need of tools for mining knowledge form databases. So, what kind of information techniques can be employed to discover hidden information of knowledge from databases is an important concern.Recently, rough-set theory has been widely used in knowledge acquisition. The rough-set theory, proposed by Pawlak in 1982 [36], is a relatively new mathematical tool for applications in circumstances which are characterized by vagueness and uncertainty. Although the rough-set theory succeeds in knowledge acquisition, but rough-set theory has been weed mostly in handling non-quantitative data. However, there are many data or information consisting of quantitative values in real world.
In this thesis, we are going to investigate how to handle quantitative data with rough set theory. Related works are reviewed. Drawbacks of previous result are overcome. A new mining procedure for quantitative data with rough set will be proposed.
中文摘要----------------------------------------------------------------------------------------Ⅲ
ENGLISH ABSTRACT----------------------------------------------------------------------Ⅳ
ACKNOWLEDGEMENTS-----------------------------------------------------------------Ⅴ
LIST OF FIGURES---------------------------------------------------------------------------Ⅶ
LIST OF TABLES----------------------------------------------------------------------------Ⅶ
CHAPTER 1 Introduction 2
1.1 Foreword 2
1.2 Background and motivation 3
1.3 Framework of thesis 5
CHAPTER 2 Rough Set Theory 6
2.1 Introduction 6
2.2 Basic concepts of the rough sets theory 7
2.2.1 Information table 7
2.2.2 Indiscernibility relation 9
2.2.3 Redundant attributes (independence of attributes) 10
2.2.4 Lower approximation and upper approximation 13
2.2.5 Accuracy of approximation 20
2.2.6 Rough membership function 21
2.3 Applications of rough set theory 22
2.4 Advantages of rough set theory 23
CHAPTER 3 Review of Wang’s Work on Quantitative Data
Mining 24
3.1 Introduction 24
3.2 An algorithm for learning the maximally general rules based on rough set 24
3.2.1 Notation 24
3.2.2 The algorithm 26
3.2.3 Illustration with example 27
3.3 An algorithm for learning the maximally general rules based on rough set and fuzzy set 32
3.3.1 Notation 32
3.3.2 The algorithm 32
3.3.3 Illustration with example 34
3.4 The drawback of Wang’s algorithm 40
CHAPTER 4 An Improved Mining Procedure 41
4.1 Introduction 41
4.2 An algorithm for mining maximal rules 41
4.2.1 Notation 41
4.2.2 Process of maximally general rules algorithm 43
4.2.3 Illustration with example 44
4.3 An algorithm for mining maximal rules from quantitative data 48
4.3.1 Notation 48
4.3.2 Process of maximally general rules algorithm for quantitative data 49
4.3.3 Illustration with example 50
4.4 The contrast with Wang’s result 57
CHAPTER 5 Conclusion 60
REFERENCES 61
LIST OF FIGURES
Figure 2-1 Lower and Upper Approximation of Set X------------------------------------16
Figure 2-2 Lower and Upper Approximation of Set X------------------------------------19
Figure 3-1 The member functions for three attributes-------------------------------------35
Figure 4-1 The member functions for three attributes------------------------------------ 50
LIST OF TABLE
Table 2-1:The Data Set for Example---------------------------------------------------------- 8
Table 2-2 Result of Classification (three attributes)--------------------------------------- 10
Table 2-3 Result of Classification (Sneeze and Temperature)----------------------------11
Table 2-4 Result of Classification (Headache and Temperature)-------------------------11
Table 2-5 Result of Classification (Headache and Sneeze)-------------------------------11
Table 2-6 Number of Elementary Sets-------------------------------------------------------12
Table 2-7 Reduced Information Table-------------------------------------------------------12
Table 2-8 New Information Table------------------------------------------------------------16
Table 3-1 Information Table-------------------------------------------------------------------25
Table 3-2 Information Table-------------------------------------------------------------------28
Table 3-3 Information Table-------------------------------------------------------------------35
Table 3-4 Information Table-------------------------------------------------------------------36
Table 4-1 Information Table-------------------------------------------------------------------42
Table 4-2 Information Table-------------------------------------------------------------------44
Table 4-3 Information Table-------------------------------------------------------------------50
Table 4-4 Information Table-------------------------------------------------------------------51
[1] A. Szladow and W. Ziarko, “Rough sets: Working with imperfect data,” AI Expert, Vol. 8, pp.36-41, 1993.
[2] A. Mrozek, “Rough sets and dependency analysis among attributes in computer implementations of expert’s inference models,” International Journal of Man-Machine Studies 30 (1989) pp.457-471.
[3] A. Skowron, “Extracting laws from decision tables,” Computational Intelligence 11/2 (1995) pp.371-388.
[4] A. J. Szladow and W. Ziarko, “Knowledge-based process control using rough sets,” Intelligent Decision Support, Handbook of Applications and Advances of the Rough Set Theory, Kluwer Academic Publishers, Dordrecht, 1992, pp.49-60.
[5] A. Skowron, “Extracting laws from decision tables — a rough set approach,” Computational Intelligence, Vol. 11, pp.371-388, 1995.
[6] B. H. Wu, “An intelligent tutoring System using a rough set approach,” Second Annual Joint Conference on Information Sciences Proceedings, September 28-October, Wrightsville Beach, North Carolina, USA, 1995, pp.409-412.
[7] B. Walczak, D. L. Massart, “Rough sets theory, ”Chemometrics and Intelligent Laboratory Systems Volume: 47, Issue: 1, April 19, 1999, pp.1-16.
[8] C. C. Chan, “A rough set approach to attribute generalization in data mining,” Information Sciences, Vol. 107, No. 1-4, pp.169-176, 1998.
[9] C. H. Wang, T. P. Hong and S. S. Tseng, “Integrating fuzzy knowledge by genetic algorithms,” IEEE Transactions on Evolutionary Computation, Vol. 2, No. 4, 1998, pp.138-149.
[10] D. Dubois and H. Prade, “Putting rough sets and fuzzy sets together,” In R. Slowinski, editor, Intelligent Decision Support — Handbook of Advances and Applications of the Rough Set Theory, pages 203-232, Kluwer Academic Publishers, Dordrecht, Boston, London, 1992.
[11] E. Krusinska, R. Slowinski, and J. Stefanowski, “Discriminant versus rough set approach to vague data analysis,” Journal of Applied Statistics and Data Analysis, Vol. 8, pp.4-56, 1992.
[12] G.. Griffin and Z. Chen, “Rough set extension of Tcl for data mining,” Knowledge-Based Systems, Vol. 11, No. 3-4, pp.249-253, 1998.
[13] J. Grzymala-Busse, “Knowledge acquisition under uncertainty — a rough set approach ,” Journal of Intelligent and Robotics Systems, Vol. 1, pp.3-16, 1988.
[14] J. Krysinski, “Rough set approach to the analysis of the structure-activity relationship of quaternary imidazolium compounds,” Arzneimittel-Forschung, (Drug Research) 40, 7 (1990), pp.795-799.
[15] J. W. Grzymala-Busse, “Knowledge acquisition under uncertainty-A rough set approach,” Journal of Intelligent and Robotic Systems 1/1 (1988) pp.3-16.
[16] L. T. Germano, P. Alexandre, “Knowledge-base reduction based on rough set techniques,” Canadian Conference on Electrical and Computer Engineering, 1996, pp.278-281.
[17] N. Zhong, J. Z. Dong, S. Ohsuga, T. Y. Lin, “An incremental, probabilistic rough
set approach to rule discovery,” IEEE International Conference on Fuzzy Systems, 1998, Vol. 2, pp.933-938.
[18] R. Slowinski, editor. Intelligent Decision Support — Handbook of Advances and Applications of the Rough Set Theory, Kluwer Academic Publishers, Dordrecht, Boston, London, 1992.
[19] R. Slowinski, “Rough set approach to decision analysis,” AI Expert, Vol. 10, pp.18-25, 1995.
[20] S. W. Changchien, and T. C. Lu, “A Data Mining Procedure Using Neural Network - Self Organization Map and Rough Set to Discover Association Rules,” Proceedings of the 2000 International Computer Symposium Workshop on Software Engineering and Database Systems, session 3, pp.83-90, 2000.
[21] S. Shenoi, “Rough sets in fuzzy databases,” Second Annual Joint Conference on Information Sciences Proceedings, September 28-October, Wrightsville Beach, North Carolina, USA, 1995, pp.348-356.
[22] T. Y. Lin and A. M. Wildberger, (eds.), The Third International Workshop on Rough Sets and Soft Computing Proceedings (RSSC’94), San Jose State University, San Jose, California, USA, November pp.10-12, 1995.
[23] T. Lin, editor. CSC’95, 23rd Annual Computer Science Conference on Rough Sets and Database Mining, San Jose, California, USA, March 1995. San Jose State University.
[24] T. P. Hong, C. S. Kuo and S. C. Chi, “Mining association rules from quantitative data”, accepted and to appear in Intelligent Data Analysis: An International Journal.
[25] T. P. Hong and J. B. Chen, “Finding relevant attributes and membership functions, “ Fuzzy Sets and Systems, Vol. 103, No. 3, 1999, pp.389-404.
[26] T. P. Hong and C. Y. Lee, “Induction of fuzzy rules and membership functions from training examples,” Fuzzy Sets and Systems, Vol. 84, 1996, pp.33-47.
[27] T. P. Hong and S. S. Tseng, “A generalized version space learning algorithm for noisy and uncertain data,” IEEE Transactions on Knowledge and Data Engineering, Vol. 9, No. 2, 1997, pp.336-340.
[28] T. P. Hong, T. T. Wang , S. L. Wang and B.C. Chien, “Learning a coverage set of maximally general fuzzy rules by rough set,” accepted and to appear in Expert Systems with Applications, 2000.
[29] W. Ziarko, Ed. “Rough sets, fuzzy sets and knowledge discovery,” In Proceedings of RSKD’94 Workshop (Banff). Springer-Verlag, Berlin 1994.
[30] W. Ziarko, “Variable Precision Rough Set Model,” Journal of Computer and System Sciences 46, 1993, pp.39-59.
[31] X. Hu and N. Cercone, “Learning in relational databases — a rough set approach,” Computational Intelligence, Vol. 11, pp.323-338, 1995.
[32] X. Hu, and N. Cercone, “Mining Knowledge Rules from Databases: A Rough Set Approach,” Proceedings of the 12th International Conference on Data Engineering, IEEE Computer Society, pp.96-105, 1996.
[33] Y. Y. Yao, “A comparative study of fuzzy sets and rough sets,” Information Sciences Volume: 109, Issue: 1-4, August, 1998, pp.227-242.
[34] Z. Pawlak, “Why rough sets?,” Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1996, Vol. 2, pp.738-743.
[35] Zdzislaw Pawlak, Jerzy Grzymala-Busse, Roman Slowinski, Wojciech Ziarko, “Rough Sets,” Communications of the ACM November 1995 Volume 38 Issue 11 pp.88-95.
[36] Z. Pawlak, “Rough Set,” International Journal of Computer and Information Sciences, pp.341-356, 1982.
[37] Z. Pawlak, “Rough Sets,” Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht, Boston, London, 1991.
[38] Z. Pawlak and R. Slowinski, “Decision analysis using rough sets,” International Transactions on Operational Research, Vol. 1, pp.107-104, 1994.
[39] Z. Pawlak, S. Wong, and W. Ziarko, “Rough Sets: Probabilistic versus deterministic approach,” International Journal of Man Machine Studies, Vol. 29, pp.81-85, 1988.
[40] Z. Pawlak and R. Slowinski, “Rough set approach to multi-attribute decision analysis,” Invited Review, Eur. J. of Oper. Res. 72 (1994), pp.443-456.
[41] 王姿婷,利用約略集合論於數量形資料之知識擷取,義守大學資訊工程研究所未出版碩士論文,民國八十九年五月.
[42] 呂慈純,資料庫關聯是規則探勘方法之研究與應用,朝陽科技大學資訊管理研究所未出版碩士論文,民國九十年五月二十五日.
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