:::

詳目顯示

回上一頁
題名:決策邏輯型機制及其在知識表徵中之應用
作者:范端芳
作者(外文):Tuan-Fang Fan
校院名稱:國立交通大學
系所名稱:資訊管理研究所
指導教授:劉敦仁
學位類別:博士
出版日期:2008
主題關鍵詞:知識管理資料探勘決策邏輯粗略集知識表徵矢決策邏輯knowledge managementdata miningdecision logicrough setknowledge representationarrow decision logic
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:24
近年來,從資料庫中發掘知識與其核心機制—資料探勘越來越受到廣泛的注意。雖然資料探勘研究一貫是以設計高效率的演算法為主,然如何使探勘所得的知識能對使用者有用,仍然持續成為該領域一個最具挑戰性的問題。由於知識能對使用者有用的先決條件是使用者能瞭解其意義,因此知識表徵機制在知識管理過程中便扮演著重要的角色。
我們在本學位論文中探討的就是從粗略集合論觀點對決策邏輯作若干擴充。傳統決策邏輯是以粗略集合論為基礎的資料探勘中一種標準的知識表徵機制,而我們的擴充顯示出決策邏輯型機制對於較複雜的知識管理工作亦非常有用。
我們一方面提出數種決策邏輯語言,可用於表達以粗略集合論為基礎的多準則決策分析方法所產生的決策法則,這些語言的語意模型為表示多準則決策記錄之資料表,其中每一個決策記錄都可以用有限多個屬性或準則來描述。而準則與屬性的差別是屬性值之間不見得有優劣關係,而準則的值之間必然存在優劣關係。
另一方面,我們提出矢決策邏輯來對關聯資訊系統中所發掘出來的知識作表徵與推理。此一邏輯係結合矢邏輯與決策邏輯的主要特徵而成,其中矢邏輯為一種用於關係推理的樣態邏輯。矢決策邏輯的邏輯式可以在關聯資訊系統中加以解釋,而關聯資訊系統不僅描述物件的屬性,亦描述其彼此之間的關係。我們提出一種矢決策邏輯的公設化系統,證明其完備性,並展現其在多準則決策分析與社交網路分析上的應用。
我們的結果對知識管理中知識表徵此一環節特別有用。我們以一個現實的例子來說明我們所提出來的機制可用來輔助公司聘僱人員及形成團隊過程中不同階段的知識表徵需求。
In recent years, knowledge discovery in databases (KDD) and its kernel data mining have received
more and more attention for practical applications. While the mainstream research of data mining
concentrates on the design of efficient algorithms for extracting knowledge from databases, the
question to close the semantic gap between structured data and human-comprehensible concepts
has been a lasting challenge for the research community. Since the discovered knowledge is useful
for a human user only when he can understand its meaning, the representation formalism will
play an important role during the knowledge management life cycle.
In this dissertation, we investigate several extensions of decision logic (DL) from the perspective
of rough set theory. Traditionally, DL has been considered as a standard way of knowledge
representation for rough set-based data mining, whereas our extensions show that DL-styled logics
are also useful in more complicated knowledge management tasks.
On the one hand, we propose some decision logic languages for rule representation in rough
set-based multicriteria decision analysis. The semantic models of these logics are data tables
representing multicriteria decision records. Each decision record is described by a finite set of
criteria/attributes. The domains of the criteria may have ordinal properties expressing preference
scales, while the domains of the attributes may not.
On the other hand, we propose an arrow decision logic (ADL) to represent and reason about
knowledge discovered from relational information systems (RIS). The logic combines the main
features of decision logic (DL) and arrow logic (AL). AL is the basic modal logic of arrows.
ADL formulas are interpreted in RIS which not only specifies the properties of objects, but also
the relationships between objects. We present a complete axiomatization of ADL and discuss
its application to knowledge representation in multicriteria decision analysis and social network
analysis.
Our work is particularly useful for the knowledge representation phase in the knowledge management
life cycle. A realistic scenario about human resource management is used to show how
the proposed logics can serve as representational formalisms in different stages of the recruitment
process and team formation process of a company.
[1] R. Agrawal and R. Srikant. Fast algorithm for mining association rules. In Proceedings of thenew window
20th International Conference on Very Large Data Bases, pages 487–499. Morgan Kaufmann
Publishers, 1994.
[2] R. Agrawal and R. Srikant. “Mining sequential patterns”. In Proc. 1995 Int. Conf. Data
Engineering (ICDE95), pages 3–14, 1995.
[3] M. Alavi and D.E. Leidner. Knowledge management and knowledge management systems:
Conceptual foundations and research issues. MIS Quarterly, 25(1):107–136, 2001.new window
[4] J.A. Allen. “Bioinformatics and discovery: induction beckons again”. BioEssays, 23:104–107,
2001.
[5] A. Asperti and G. Longo. Categories, Types, and Structures: An Introduction to Category
Theory for the Working Computer Scientist. MIT Press, 1991.
[6] G. Birkhoff and J. Lipson. Heterogeneous algebras. Journal of Combinatorial Theory, 8,
1970.
[7] P. Blackburn, M. de Rijke, and Y. Venema. Modal Logic. Cambridge Uinversity Press, 2001.
[8] S.N. Burris and H.P. Sankappanavar. A Course in Universal Algebra. Springer-Verlag, 1981.
[9] R. Carnap. Logical Foundations of Probability. The University of Chicago Press, Routledge,
1962.
[10] C.F. Chien and L.F. Chen. Using rough set theory to recruit and retain high-potential talents
for semiconductor manufacturing. IEEE Transactions On Semiconductor Manufacturing,
20(4), 2007.
[11] P. M. Cohn. Universal Algebra. D. Reidel Publishing Co., 1981.
[12] L. Darden. “Anomaly-driven theory redesign: computational philosophy of science experiments”.
In T.W. Bynum and J.H. Moor, editors, The Digital Phoenix: How Computers are
Changing Philosophy, pages 62–78. Blackwell Publishers, 1998.
[13] K. Dembczynski, S. Greco, and R. Slowinski. Methodology of rough-set-based classification
and sorting with hierarchical structure of attributes and criteria. Control & Cybernetics,
31:891–920, 2002.
[14] S. Demri and E. Orlowska. Incomplete Information: Structure, Inference, Complexity.
EATCS Monographs. Springer-Verlag, 2002.
[15] Y. Dimopoulos and A. Kakas. “Abduction and inductive learning”. In L. De Raedt, editor,
Advances in Inductive Logic Programming, pages 144–171. IOS Press, 1996.
[16] T.F. Fan, W.C. Hu, and C.J. Liau. Decision logics for knowledge representation in data
mining. In Proceedings of the 25th Annual International Computer Software and Applications
Conference, pages 626–631. IEEE Press, 2001.
[17] T.F. Fan and C.J. Liau. The justification problem of data mining—a decision logic formulation
and its implications. In Proc. of the IEEE ICDM2002 Workshop on the Foundation of Data
Mining and Knowledge Discovery, pages 113–118, 2002.
[18] T.F. Fan, C.J. Liau, D.R. Liu, and G.H. Tzeng. Granulation based on hybrid information
systems. In Proceedings of the 2006 IEEE International Conference on Systems, Man, and
Cybernetics, pages 4768–4772, 2006.
[19] T.F. Fan, D.R. Liu, and C.J. Liau. “Justification and hypothesis selection in data mining”.
In T.Y. Lin, S. Ohsuga, C.J. Liau, X. Hu, and S. Tsumoto, editors, Foundations of Data
Mining and Knowledge Discovery, pages 119–130. Springer, 2005.
[20] T.F. Fan, D.R. Liu, and G.H. Tzeng. Arrow decision logic for relational information systems.
LNCS Transactions on Rough Sets, V(LNCS 4100):240–262, 2006.
[21] T.F. Fan, D.R. Liu, and G.H. Tzeng. Rough set-based logics for multicriteria decision analysis.
European Journal of Operational Research, 182(1):340–355, 2007.new window
[22] Z. Farkas. System integration via logic in knowledge management — a project initiative.
[23] P. Flach. An Inquiry Concerning the Logic of Induction. ITK Dissertation Series, 1995.
[24] P. Flach and A. Kakas. “On the relation between abduction and inductive learning”. In D.M.
Gabbay and R. Kruse, editors, Handbook of Defeasible Reasoning and Uncertainty Management
Systems Vol. 4: Abductive Reasoning and Learning, pages 1–33. Kluwer Academic
Publishers, 2000.
[25] D.M. Gabbay. An irreflexivity lemma with applications to axiomatization of conditions on
linear frames. In U. M¨onnich, editor, Aspects of Philosophical Logic, pages 67–89. D. Reidel
Publishing Co., 1981.
[26] F. Giannotti, G. Manco, D. Pedreschi, and F. Turini. “Experiences with a logic-based knowledge
discovery support environment”. In E. Lamma and P. Mello, editors, AI*IA 99: Advances
in Artificial Intelligence, 6th Congress of the Italian Association for Artificial Intelligence,
LNAI 1792, pages 202–213. Springer-Verlag, 1999.
[27] D. Gillies. Artificial Intelligence and Scientific Method. Oxford University Press, 1996.
[28] N. Goodman. Fact, Fiction, and Forecast. Bobbs-Merrill, Indianapolis, third edition, 1971.
[29] S. Greco, B. Matarazzo, and R. Slowinski. Rough set approach to multi-attribute choice and
ranking problems. In Proceedings of the 12th International Conference on Multiple Criteria
Decision Making, pages 318–329, 1997.
[30] S. Greco, B. Matarazzo, and R. Slowinski. Rough approximation of a preference relation in
a pairwise comparison table. In L. Polkowski and A. Skowron, editors, Rough Sets in Data
Mining and Knowledge Discovery, pages 13–36. Physica-Verlag, 1998.
[31] S. Greco, B. Matarazzo, and R. Slowinski. Rough approximation of a preference relation by
dominance relations. European Journal of Operational Research, 117(1):63–83, 1999.new window
[32] S. Greco, B. Matarazzo, and R. Slowinski. The use of rough sets and fuzzy sets in MCDM.
In T. Gal, T. Hanne, and T. Stewart, editors, Multicriteria Decision Making: Advances in
MCDM Models, Algorithms, Theory and Applications, pages 14.1–14.59. Kluwer Academic
Publishers, 1999.
[33] S. Greco, B. Matarazzo, and R. Slowinski. Extension of the rough set approach to multicriteria
decision support. INFOR Journal: Information Systems and Operational Research,
38(3):161–195, 2000.
[34] S. Greco, B. Matarazzo, and R. Slowinski. Rough set theory for multicriteria decision analysis.
European Journal of Operational Research, 129(1):1–47, 2001.new window
[35] S. Greco, B. Matarazzo, and R. Slowinski. Rough sets methodology for sorting problems
in presence of multiple attributes and criteria. European Journal of Operational Research,
138(2):247–259, 2002.
[36] S. Greco, B. Matarazzo, and R. Slowinski. Axiomatic characterization of a general utility
function and its particular cases in terms of conjoint measurement and rough-set decision
rules. European Journal of Operational Research, 158(2):271–292, 2004.
[37] S. Greco, B. Matarazzo, R. Slowinski, and J. Stefanowski. Variable consistency model of
dominance-based rough set approach. In W. Ziarko and Y. Yao, editors, Proceedings of the
2nd International Conference on Rough Sets and Current Trends in Computing, LNAI 2005,
pages 170–181. Springer-Verlag, 2001.
[38] P. Gr¨unwald, I.J. Myung, and M. Pitt. Advances in Minimum Description Length: Theory
and Applications. The MIT Press, 2004.
[39] J.W. Grzymala-Busse. Algebraic properties of knowledge representation systems. In Proceedings
of the ACM SIGART International Symposium on Methodologies for Intelligent Systems,
pages 432–440. ACM Press, 1986.
[40] P. H´ajek. Metamathematics of Fuzzy Logic. Kluwer Academic Publisher, 1998.
[41] J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers,
2001.
[42] J. Hawthorne. Knowledge and Lotteries. Oxford University Press, 2003.
[43] J. Hawthorne and L. Bovens. “The preface, the lottery, and rational belief”. Mind, 108:241–
264, 1999.
[44] C.G. Hempel. “Studies in the logic of confirmation”. In C. Hempel, editor, Aspects of
Scienfific Explanation and Other Essays in the Philosophy of Science, pages 3–46. Free Press,
New York, 1965.
[45] C.G. Hempel. “Studies in the logic of confirmation”. In C. Hempel, editor, Aspects of Scienfific
Explanation and Other Essays in the Philosophy of Science, pages 53–79. Free Press, New
York, 1965.
[46] J. Hintikka and P. Suppes. Aspects of Inductive Logic. North Holland, Amsterdam, 1967.
[47] D. Hofstadter and Fluid Analogies Research Group. Fluid Concepts and Creative Analogies:
Computer Models of the Fundamental Mechanisms of Thought. Penguin, 1998.
[48] D. Hume. A Treatise of Human Nature. Clarendon Press, Oxford, 1896.
[49] C.L. Hwang and K. Yoon. Multiple Attribute Decision Making : Methods and Applications.
Springer-Verlag, 1981.
[50] R. Kruse, C. Borgelt, and D. Nauck. Fuzzy data analysis: challenges and perspectives. In
Proceedings of the 8th IEEE International Conference on Fuzzy Systems, pages 1211–1216,
San Francisco, CA, 1999. IEEE.
[51] M. Kryszkiewicz. Properties of incomplete information systems in the framework of rough
sets. In L. Polkowski and A. Skowron, editors, Rough Sets in Knowledge Discovery, pages
422–450. Physica-Verlag, 1998.
[52] M. Kryszkiewicz and H. Rybi´nski. Reducing information systems with uncertain attributes.
In Z. W. Ra´s and M. Michalewicz, editors, Proceedings of the 9th ISMIS, LNAI 1079, pages
285–294. Springer-Verlag, 1996.
[53] M. Kryszkiewicz and H. Rybi´nski. Reducing information systems with uncertain real value
attributes. In Proceedings of the 6th International Conference on Information Processing and
Management of Uncertainty in Knowledge-based Systems, pages 1165–1169, 1996.
[54] H. Kyburg. Probability and the Logic of Rational Belief. Wesleyan University Press, Middletown,
1961.
[55] C.J. Liau. Belief reasoning, revision and fusion by matrix algebra. In J. Komorowski and
S. Tsumoto, editors, Proceedings of the 4th International Conference on Rough Sets and
Current Trends in Computing, LNAI 3066, pages 133–142. Springer-Verlag, 2004.
[56] C.J. Liau. Matrix representation of belief states: An algebraic semantics for belief logics.
International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, 12(5):613–
633, 2004.
[57] C.J. Liau and T.Y. Lin. Reasoning about relational granulation in modal logics. In Proc.
of the First IEEE International Conference on Granular Computing, pages 534–538. IEEE
Press, 2005.
[58] C.J. Liau and D.R. Liu. A logical approach to fuzzy data analysis. In J.M. Zytkow and
J. Rauch, editors, Proceedings of the 3rd European Conference on Principles of Data Mining
and Knowledge Discovery, LNAI 1704, pages 412–417. Springer-Verlag, 1999.
[59] C.J. Liau and D.R. Liu. A possibilistic decision logic with applications. Fundamenta Informaticae,
46(3):199–217, 2001.
[60] W. Lipski. On databases with incomplete information. Journal of the ACM, 28(1):41–70,new window
1981.
[61] M. Marx and Y. Venema. Multi-Dimensional Modal Logic. Kluwer Academic Publishers,
1997.
[62] J. McCarthy. Phenomenal data mining. Communications of the ACM, 43(8):75–79, 2000.
[63] E. Mendelson. Introduction to Mathematical Logic. Chapman & Hall/CRC, forth edition,
1997.
[64] S. Muggleton and L. de Raedt. “Inductive logic programming: theory and methods”. Journal
of Logic Programming, 19/20:629–679, 1994.
[65] D.K. Nelkin. “The lottery paradox, knowledge, and rationality”. The Philosophical Review,
109(3):373–409, 2000.
[66] I. Nonaka. A dynamic theory of organizational knowledge creation. Organization Science,
5(1):14–37, 1994.new window
[67] Z. Pawlak. Rough Sets–Theoretical Aspects of Reasoning about Data. Kluwer Academic
Publishers, 1991.
[68] L. Polkowski, S. Tsumoto, and T.Y. Lin, editors. Rough Set Methods and Applications.
Physica-Verlag, 2000.
[69] D. Poole. “Learning, Bayesian probability, graphical models, and abduction”. In P. Flach
and A. Kakas, editors, Abduction and Induction: Essays on Their Relation and Integration.
Kluwer Academic Publishers, 1998.
[70] K. R. Popper. The Logic of Scientific Discovery. Harper & Row, New York, 1968.
[71] J. Rissanen. “Modeling by shortest data description”. Automatica, 14:465–471, 1978.
[72] A. Satyadas, U. Harigopal, and N.P. Cassaigne. Knowledge management tutorial: an editorial
overview. IEEE Transactions on Systems, Man, and Cybernetics–Part C: Applications and
Reviews, 31(4):429–437, 2001.
[73] P. Schroeder-Heister. “Popper, Karl Raimund”. In International Encyclopedia of the Social
and Behavioral Sciences. Elsevier, 2001.
[74] J. Scott. Social Network Analysis: A Handbook. SAGE Publications, 2 edition, 2000.
[75] A. Skoworn and L. Polkowski, editors. Rough Sets In Knowledge Discovery Vol 1: Methodologynew window
and Applications. Physica-Verlag, 1998.
[76] A. Skoworn and L. Polkowski, editors. Rough Sets In Knowledge Discovery Vol 2: Applications,
Case Studies and Software Systems. Physica-Verlag, 1998.
[77] A. Skowron. Extracting laws from decision tables. Computational Intelligence: An International
Journal, 11:371–388, 1995.
[78] A. Skowron and C. Rauszer. The discernibility matrices and functions in information systems.
In R. Slowinski, editor, Intelligent Decision Support Systems: Handbook of Applications and
Advances in Rough Set Theory, pages 331–362. Kluwer Academic Publisher, 1991.
[79] A. Skowron and R. Swiniarski. Rough sets and higher order vagueness. In D. Slezak, G.Y.
Wang, M. Szczuka, I. Duntsch, and Y.Y. Yao, editors, Proceedings of the 10th International
Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, LNAI 3641,
pages 33–42. Springer-Verlag, 2005.
[80] A. Skowron and P. Synak. Complex patterns. Fundamenta Informaticae, 60(1-4):351–366,
2004.
[81] R. Slowinski, S. Greco, and B. Matarazzo. Mining decision-rule preference model from rough
approximation of preference relation. In Proceedings of the 26th IEEE Annual International
Conference on Computer Software and Applications, pages 1129–1134. IEEE Press, 2002.
[82] R. Slowinski, S. Greco, and B. Matarazzo. Rough set analysis of preference-ordered data.
In J.J. Alpigini, J.F. Peters, A. Skowron, and N. Zhong, editors, Proceedings of the 3rd
International Conference on Rough Sets and Current Trends in Computing, LNAI 2475, pages
44–59. Springer-Verlag, 2002.
[83] M.H. Stone. The theory of representations of boolean algebras. Transactions of the American
Mathematical Society, 40:37–111, 1936.
[84] P. Thagard. Computational Philosophy of Science. The MIT Press, 1988.
[85] P. Thagard. Conceptual Revolutions. PrincetonUniversity Press, 1992.
[86] P. Thagard. “Probabilistic networks and explanatory coherence”. Cognitive Science Quarterly,
1:91–144, 2000.new window
[87] Y. Venema. A crash course in arrow logic. In M. Marx, L. P’olos, and M. Masuch, editors,
Arrow Logic and Multi-Modal Logic, pages 3–34. CSLI Publications, 1996.
[88] R.H. Warren, J.A. Johnson, and G.H. Huang. Application of rough sets to environmental
engineering. LNCS Transactions on Rough Sets, 1:356–374, 2004.new window
[89] P. Watson. “Inductive learning with corroboration”. In O. Watanabe and T. Yokomori,
editors, Proceedings of the 10th International Conference on Algorithmic Learning Theory,
LNAI 1720, pages 145–156. Springer-Verlag, 1999.
[90] J. Williamson. Machine learning and the philosophy of science: a dynamic interaction. In Proceedings
of the ECML-PKDD-01 Workshop on Machine Leaning as Experimental Philosophy
of Science, 2001.
[91] T.D. Wilson. The nonsense of “knowledge management”. Information Research, 8(1), 2002.new window
[92] W.W. Wu, Y.T. Lee, and G.H. Tzeng. Simplifying the manager competency model by using
the rough set approach. In D.Slezak, J. Yao, J.F. Peters, W. Ziarko, and X. Hu, editors,
Proceedings of the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining,
and Granular Computing, LNAI 3642, pages 484–494. Springer-Verlag, 2005.
[93] Y.Y. Yao and C.J. Liau. A generalized decision logic language for granular computing. In
Proceedings of the 11th IEEE International Conference on Fuzzy Systems, pages 773–778.
IEEE Press, 2002.
[94] Y.Y. Yao and Q. Liu. A generalized decision logic in interval-set-valued information tables. In
N. Zhong, A. Skowron, and S. Ohsuga, editors, New Directions in Rough Sets, Data Mining,
and Granular-Soft Computing, LNAI 1711, pages 285–293. Springer-Verlag, 1999.
[95] Y.Y. Yao and N. Zhong. An analysis of quantitative measures associated with rules. In
Proceedings of the 2nd Pacific-Asia Conference on Knowledge Discovery and Data Mining,
pages 479–488. IEEE Press, 1999.
[96] L.A. Zadeh. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1):3–28,new window
1978.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE