TIẾP CẬN LÝ THUYẾT TẬP THÔ TRONG KHAI PHÁ DỮ LIỆU
Abstract
Lý thuyết tập thô được phát triển trên một nền tảng toán học vững chắc, cung cấp những công cụ hữu ích để giải quyết các bài toán phân tích dữ liệu, phát hiện luật … và đặc biệt thích hợp đối những bài toán chứa đựng thông tin mơ hồ, không chắc chắn. Trong phần trình bày này, chúng tôi sẽ chỉ ra một số cách tiếp cận lý thuyết tập thô trong các kỹ thuật khai phá dữ liệu cơ bản và hướng nghiên cứu liên quan.References
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[2] DANIEL DELIC, HANS-J. LENZ, and MATTIS NEILING (2002),“Improving the Quality of Association Rule Mining by Means of Rough Sets”, Free University of Berlin, Institute of Applied Computer Science, Berlin, Germany.
[3] ERIC C.C.TSANG, DEGANG CHEN, JOHN W.T.LEE, DANIEL S.YEUNG (2004), “On The Upper Approximations of covering generalized Rough sets”, Proceeding of the Third International Conference on Machine Learning and Cybernetic, Shanghai, 26-29 August 2004.
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[11] JERZY W. GRZYMALA-BUSSE (2004), “Three Approaches to Missing Attribute Values—A Rough Set Perspective”, Accepted for the Workshop on Foundations of Data Mining, associated with the fourth IEEE International Conference on Data Mining, Brighton, UK, November 1–4, 2004.
[12] JIYE LI (2007), “Rough Set Based Rule Evaluations and Their Applications”. PhD thesis, University of Waterloo, Ontario, Canada, pp.41-111.
[13] JIYE LI, NICK CERCONE (2010), “A method of discovering important rules using rules as attributes”, International Journal of Intelligent Systems, Volume 25, Issue 2, pages 180–206, February 2010.
[14] JIYE LIANG, JUNHONG WANG (2006), “A new Lattice Structure and Method for Extracting Association rules based on Concept Lattice”, IJCSNS International Journal of Computer Science and Network Security, Vol. 6, No. 11, November, 2006.
[15] JUHUA QIAN, YIYE LIANG (2008), “A new Method for Measuring the Uncertainty in Incomplete Information System”, International Journal of Uncertainty, Fuzziness and Knowledge Based System, World Scientific Publish Company.
[16] KLEIN M. C. A., MIKA P., SCHLOBACH S. (2007), ”Rough Description Logics for Modeling Uncertainty in Instance Unification”, Proceedings of the Workshop on Uncertainty Reasoning for the Semantic Web URSW, 1-12.
[17] LICAI YANG, LANCANG YANG (2006), “Study of a Cluster Algorithm Based on Rough Set Theory”, Proceeding of Sixth Internatioanl Conference on Intelligent Systems Design and Application (ISDA’06), IEEE.
[18] LUO Ke, WANG Li-li, TONG Xiao-jiao, “Mining association rules in incomplete information system”, J. Cent. South Univ. Technology. (2008) 15: 733−737.
[19] MANISHA TANTUWAY (2011), “An Efficient Decision Tree Algorithm Using Rough Set Theory, Vol 2, No 2”, International Journal of Advanced Research in Computer Science (IJARCS).
[20] NGUYỄN ĐỨC THUẦN, NGUYỄN XUÂN HUY (2009), “A new measure to evaluate the consistency of a set of decision rules extracted from a decision table”, International Journal of Computer Electrical Engineering (IJCEE).
[21] NGUYEN, H. S. (2003), “On the Decision Table with Maximal Number of Reducts”, In: Electric Notes in Theorical Computer Science 82 No4 (2003), URL: http://www.elsevier.nl/locate/entcs/volume82.html.
[22] NGUYEN, H. S., & SKOWRON, A. (1997B). “Boolean Reasoning for Feature Extraction Problems”. In Proceedings of the 10th International Symposium on Methodologies for Intelligent Systems, pp. 117–126.
[23] PHAN THANH LIÊM (2004), “Ứng dụng mô hình tập thô dung sai trong xử lý văn bản tiếng Việt”, Đồ án t t nghiệp, ĐHBK Hà Nội.
[24] SCHLOBACH S., KLEIN M., PEELEN L. (2007), “Description Logics with Approximate Definitions: Precise Modeling of Vague Concepts”, In Proc. of the 20th Int. Joint Conf. on Art. Intel., IJCAI 07, Hyderabad, India, 557-562.
[25] SHUSAKU TSUMOTO (2005), “Discovery of Positive and Negative Rules from Medical Databases Based on Rough Sets”, Advanced Techniques in Knowledge Discovery and Datamining, 233-250.
[26] TINGHUAI MA, JIAZHAO LENG, MENGMENG CUI AND WEI TIAN (2009), “Inducing Positive and Negative Rules Based on Rough Set”, Infomation Technology Journal 8(7): 1039-1043.
[27] WILLIAM ZHU (2006), FEI-YUE WANG, “Relationships among Three Type of Covering Rough Sets”, IEEE GrC, Atlanda, USA, pp:43-48, 2006.
[28] WILLIAM ZHU (2006), “Properties of the Second Type of Covering-Based Rough Sets”, Proceedings of the 2006 IEEE/ACM International Conference on WI-IAT 2006 Workshops.
[29] WILLIAM ZHU (2006), “Topological approaches to covering rough sets”, Information Science, in press 2006.
[30] WILLIAM ZHU, FEI-YUE WANG (2003), “Reduction and axiomization of covering generalized rough sets”, Information Science,152 217-230.
[31] WOJCIECH JAWORSKI (2008), “Rule Induction: Combining Rough Set and Statistical Aproaches. C.-C. Chan et al. (Eds.)”: RSCTC 2008, LNAI 5306, pp. 170–180, 2008._c Springer Verlag Berlin Heidelberg 2008.
[32] Y.Y.YAO (2004), “Concept Lattices in Rough Set Theory”, In Fuzzy Information, 2004, Proceedings NAFIP’04, IEEE.
[33] Z.PAWLAK (1991), “Rough sets, Theorical Aspects of Reasoning about Data”, Kluwer Academic Publishers, London.
[2] DANIEL DELIC, HANS-J. LENZ, and MATTIS NEILING (2002),“Improving the Quality of Association Rule Mining by Means of Rough Sets”, Free University of Berlin, Institute of Applied Computer Science, Berlin, Germany.
[3] ERIC C.C.TSANG, DEGANG CHEN, JOHN W.T.LEE, DANIEL S.YEUNG (2004), “On The Upper Approximations of covering generalized Rough sets”, Proceeding of the Third International Conference on Machine Learning and Cybernetic, Shanghai, 26-29 August 2004.
[4] GEORG PETERS (2006), “Some refinements of rough k-means clustering”, Elseviers, Pattern Recognition 39 (2006) 1481 – 1491.
[5] HAN, J., HU, X., LIN, T. Y. (2004). “Feature Subset Selection Based on Relative Dependency between Attributes, Rough sets and Current trends in Computing Lecture Notes” in Computer Science Vol 3066, 2004, pp 176-185.
[6] HỒ THUẦN, HOÀNG THỊ LAN GIAO (2005), “Một thuật toán tìm tập rút gọn sử dụng ma trận phân biệt được”, Chuyên san các công trình nghiên c u triển khai Viễn thông và CNTT, s 15(12/2005),83-87.
[7] HO, T.B, KAWASAKI, S. NGUYEN, N.B, (2002), “Cluster-based Information Retrieval with Tolerance Rough Set Model”, International Journal of Fuzzy Logic and Inteligent Systems, Vol. 2, No. 1,26-32.
[8] HO, T.B, NGUYEN, N.B, (2002), “Nonhierachical Document Clustering by a Tolerance Rough Set Model”, International Journal of Fuzzy Logic and Inteligent Systems, Vol. 17, No. 2, 199-212.
[9] J.W.GUAN, D.A.BELL, D.Y.LIU (2003), “The Rough Set Approach to Association Rule Mining”, Procceding of the Third IEEE International Conference of Data Mining (ICDM’03).
[10] JERZY W. GRZYMALA-BUSSE (2003), “Rough Set Strategies to Data with Missing Attribute Values”, Proceedings of the Workshop on Foundations and New Directions in Data Mining, associated with the third IEEE International Conference on Data Mining, November 19–22, 2003, Melbourne, FL, USA, 56–63.
[11] JERZY W. GRZYMALA-BUSSE (2004), “Three Approaches to Missing Attribute Values—A Rough Set Perspective”, Accepted for the Workshop on Foundations of Data Mining, associated with the fourth IEEE International Conference on Data Mining, Brighton, UK, November 1–4, 2004.
[12] JIYE LI (2007), “Rough Set Based Rule Evaluations and Their Applications”. PhD thesis, University of Waterloo, Ontario, Canada, pp.41-111.
[13] JIYE LI, NICK CERCONE (2010), “A method of discovering important rules using rules as attributes”, International Journal of Intelligent Systems, Volume 25, Issue 2, pages 180–206, February 2010.
[14] JIYE LIANG, JUNHONG WANG (2006), “A new Lattice Structure and Method for Extracting Association rules based on Concept Lattice”, IJCSNS International Journal of Computer Science and Network Security, Vol. 6, No. 11, November, 2006.
[15] JUHUA QIAN, YIYE LIANG (2008), “A new Method for Measuring the Uncertainty in Incomplete Information System”, International Journal of Uncertainty, Fuzziness and Knowledge Based System, World Scientific Publish Company.
[16] KLEIN M. C. A., MIKA P., SCHLOBACH S. (2007), ”Rough Description Logics for Modeling Uncertainty in Instance Unification”, Proceedings of the Workshop on Uncertainty Reasoning for the Semantic Web URSW, 1-12.
[17] LICAI YANG, LANCANG YANG (2006), “Study of a Cluster Algorithm Based on Rough Set Theory”, Proceeding of Sixth Internatioanl Conference on Intelligent Systems Design and Application (ISDA’06), IEEE.
[18] LUO Ke, WANG Li-li, TONG Xiao-jiao, “Mining association rules in incomplete information system”, J. Cent. South Univ. Technology. (2008) 15: 733−737.
[19] MANISHA TANTUWAY (2011), “An Efficient Decision Tree Algorithm Using Rough Set Theory, Vol 2, No 2”, International Journal of Advanced Research in Computer Science (IJARCS).
[20] NGUYỄN ĐỨC THUẦN, NGUYỄN XUÂN HUY (2009), “A new measure to evaluate the consistency of a set of decision rules extracted from a decision table”, International Journal of Computer Electrical Engineering (IJCEE).
[21] NGUYEN, H. S. (2003), “On the Decision Table with Maximal Number of Reducts”, In: Electric Notes in Theorical Computer Science 82 No4 (2003), URL: http://www.elsevier.nl/locate/entcs/volume82.html.
[22] NGUYEN, H. S., & SKOWRON, A. (1997B). “Boolean Reasoning for Feature Extraction Problems”. In Proceedings of the 10th International Symposium on Methodologies for Intelligent Systems, pp. 117–126.
[23] PHAN THANH LIÊM (2004), “Ứng dụng mô hình tập thô dung sai trong xử lý văn bản tiếng Việt”, Đồ án t t nghiệp, ĐHBK Hà Nội.
[24] SCHLOBACH S., KLEIN M., PEELEN L. (2007), “Description Logics with Approximate Definitions: Precise Modeling of Vague Concepts”, In Proc. of the 20th Int. Joint Conf. on Art. Intel., IJCAI 07, Hyderabad, India, 557-562.
[25] SHUSAKU TSUMOTO (2005), “Discovery of Positive and Negative Rules from Medical Databases Based on Rough Sets”, Advanced Techniques in Knowledge Discovery and Datamining, 233-250.
[26] TINGHUAI MA, JIAZHAO LENG, MENGMENG CUI AND WEI TIAN (2009), “Inducing Positive and Negative Rules Based on Rough Set”, Infomation Technology Journal 8(7): 1039-1043.
[27] WILLIAM ZHU (2006), FEI-YUE WANG, “Relationships among Three Type of Covering Rough Sets”, IEEE GrC, Atlanda, USA, pp:43-48, 2006.
[28] WILLIAM ZHU (2006), “Properties of the Second Type of Covering-Based Rough Sets”, Proceedings of the 2006 IEEE/ACM International Conference on WI-IAT 2006 Workshops.
[29] WILLIAM ZHU (2006), “Topological approaches to covering rough sets”, Information Science, in press 2006.
[30] WILLIAM ZHU, FEI-YUE WANG (2003), “Reduction and axiomization of covering generalized rough sets”, Information Science,152 217-230.
[31] WOJCIECH JAWORSKI (2008), “Rule Induction: Combining Rough Set and Statistical Aproaches. C.-C. Chan et al. (Eds.)”: RSCTC 2008, LNAI 5306, pp. 170–180, 2008._c Springer Verlag Berlin Heidelberg 2008.
[32] Y.Y.YAO (2004), “Concept Lattices in Rough Set Theory”, In Fuzzy Information, 2004, Proceedings NAFIP’04, IEEE.
[33] Z.PAWLAK (1991), “Rough sets, Theorical Aspects of Reasoning about Data”, Kluwer Academic Publishers, London.
Published
2014-11-26
How to Cite
THUẦN, Nguyễn Đức.
TIẾP CẬN LÝ THUYẾT TẬP THÔ TRONG KHAI PHÁ DỮ LIỆU.
JBIS, [S.l.], nov. 2014.
Available at: <http://jbis.ueh.edu.vn/index.php/TSTHQL/article/view/22>. Date accessed: 27 apr. 2024.
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Bài viết
Keywords
lý thuyết tập thô
