Approximate Boolean Reasoning: Foundations and Applications in Data Mining
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[1] Hung Son Nguyen,et al. Approximate Boolean Reasoning Approach to Rough Sets and Data Mining , 2005, RSFDGrC.
[2] Huan Liu,et al. Chi2: feature selection and discretization of numeric attributes , 1995, Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence.
[3] Salvatore Greco,et al. Fuzzy Similarity Relation as a Basis for Rough Approximations , 1998, Rough Sets and Current Trends in Computing.
[4] Sinh Hoa Nguyen,et al. Learning Sunspot Classification , 2006, Fundam. Informaticae.
[5] Z. Pawlak. Classification of objects by means of attributes , 1981 .
[6] A. P. Bowran. A Boolean Algebra , 1965 .
[7] Alan Bundy,et al. Constructing Induction Rules for Deductive Synthesis Proofs , 2006, CLASE.
[8] Tom Fawcett,et al. Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions , 1997, KDD.
[9] Andrzej Skowron,et al. Rough sets and Boolean reasoning , 2007, Inf. Sci..
[10] Alberto Maria Segre,et al. Programs for Machine Learning , 1994 .
[11] Z. Pawlak. Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .
[12] Hector J. Levesque,et al. A New Method for Solving Hard Satisfiability Problems , 1992, AAAI.
[13] Zbigniew Michalewicz,et al. Genetic algorithms + data structures = evolution programs (2nd, extended ed.) , 1994 .
[14] Andrzej Skowron,et al. Layered Learning for Concept Synthesis , 2004, Trans. Rough Sets.
[15] Ron Kohavi,et al. Lazy Decision Trees , 1996, AAAI/IAAI, Vol. 1.
[16] Heikki Mannila,et al. Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.
[17] Hung Son Nguyen,et al. Efficient SQL-Querying Method for Data Mining in Large Data Bases , 1999, IJCAI.
[18] N. Dunford,et al. On the Representation Theorem for Boolean Algebras , 1944 .
[19] Sadaaki Miyamoto,et al. Rough Sets and Current Trends in Computing , 2012, Lecture Notes in Computer Science.
[20] S. K. Michael Wong,et al. Rough Sets: Probabilistic versus Deterministic Approach , 1988, Int. J. Man Mach. Stud..
[21] Jason Catlett,et al. On Changing Continuous Attributes into Ordered Discrete Attributes , 1991, EWSL.
[22] Yves Kodratoff,et al. Machine Learning — EWSL-91 , 1991, Lecture Notes in Computer Science.
[23] Hung Son Nguyen,et al. On Efficient Handling of Continuous Attributes in Large Data Bases , 2001, Fundam. Informaticae.
[24] S. Greco,et al. Data mining tasks and methods: Classification: multicriteria classification , 2002 .
[25] William W. Cohen. Learning Trees and Rules with Set-Valued Features , 1996, AAAI/IAAI, Vol. 1.
[26] Hung Son Nguyen,et al. From Optimal Hyperplanes to Optimal Decision Trees , 1998, Fundam. Informaticae.
[27] Usama M. Fayyad,et al. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.
[28] Mohammed J. Zaki. Efficient enumeration of frequent sequences , 1998, CIKM '98.
[29] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[30] Simon Kasif,et al. Induction of Oblique Decision Trees , 1993, IJCAI.
[31] H. Peter Gray. Universidad Politechnica de Madrid , 1981 .
[32] Wojciech Ziarko,et al. Rough Sets and Knowledge Discovery: An Overview , 1993, RSKD.
[33] Z. Pawlak,et al. A Rough Set Perspective on Data andKnowledge ? , 1999 .
[34] Janusz Zalewski,et al. Rough sets: Theoretical aspects of reasoning about data , 1996 .
[35] Jorma Rissanen,et al. SLIQ: A Fast Scalable Classifier for Data Mining , 1996, EDBT.
[36] Sinh Hoa Nguyen,et al. Pattern Extraction from Data , 1998, Fundam. Informaticae.
[37] Martin Anthony,et al. Computational learning theory: an introduction , 1992 .
[38] Andrzej Skowron,et al. Boolean Reasoning for Decision Rules Generation , 1993, ISMIS.
[39] E. V. Huntington. Boolean Algebra. A Correction , 1933 .
[40] Andrzej Skowron,et al. Situation Identification by Unmanned Aerial Vehicle , 2000, Rough Sets and Current Trends in Computing.
[41] Matthias Hagen,et al. Complexity of DNF and Isomorphism of Monotone Formulas , 2005, MFCS.
[42] Karem A. Sakallah,et al. GRASP—a new search algorithm for satisfiability , 1996, ICCAD 1996.
[43] H. P. Williams,et al. Logic-Based Decision Support: Mixed Integer Model Formulation , 1989 .
[44] Donald W. Loveland,et al. A machine program for theorem-proving , 2011, CACM.
[45] Eugene Goldberg,et al. BerkMin: A Fast and Robust Sat-Solver , 2002 .
[46] Hilary Putnam,et al. A Computing Procedure for Quantification Theory , 1960, JACM.
[47] K. Scarbrough,et al. of Electrical Engineering , 1982 .
[48] Jerzy W. Grzymala-Busse,et al. Global discretization of continuous attributes as preprocessing for machine learning , 1996, Int. J. Approx. Reason..
[49] Ryszard S. Michalski,et al. Discovering Classification Rules Using variable-Valued Logic System VL1 , 1973, IJCAI.
[50] Qiang Shen,et al. Finding Rough Set Reducts with SAT , 2005, RSFDGrC.
[51] Andrzej Skowron,et al. Rough-Neural Computing: Techniques for Computing with Words , 2004, Cognitive Technologies.
[52] J. Stepaniuk. Approximation Spaces, Reducts and Representatives , 1998 .
[53] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[54] Wojciech Ziarko,et al. Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..
[55] Andrzej Skowron,et al. Rough Set Methods in Approximation of Hierarchical Concepts , 2004, Rough Sets and Current Trends in Computing.
[56] Vasco M. Manquinho,et al. Prime implicant computation using satisfiability algorithms , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.
[57] Robert A. Kowalski,et al. Logic for problem solving , 1982, The computer science library : Artificial intelligence series.
[58] Frederick Hayes-Roth,et al. Building expert systems , 1983, Advanced book program.
[59] Marcin S. Szczuka,et al. RSES and RSESlib - A Collection of Tools for Rough Set Computations , 2000, Rough Sets and Current Trends in Computing.
[60] Sinh Hoa Nguyen,et al. Fast split selection method and its application in decision tree construction from large databases , 2005, Int. J. Hybrid Intell. Syst..
[61] Hung Son Nguyen,et al. Text Classification Using Lattice Machine , 1999, ISMIS.
[62] Andrzej Skowron,et al. Applications, case studies and software systems , 1998 .
[63] M. Stone. The theory of representations for Boolean algebras , 1936 .
[64] Hung Son Nguyen,et al. On Exploring Soft Discretization of Continuous Attributes , 2004, Rough-Neural Computing: Techniques for Computing with Words.
[65] Warren S. Sarle,et al. Stopped Training and Other Remedies for Overfitting , 1995 .
[66] Sinh Hoa Nguyen,et al. Regularity analysis and its applications in data mining , 2000 .
[67] Rakesh Agarwal,et al. Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.
[68] Andrzej Skowron,et al. Discovery of Data Patterns with Applications to Decomposition and Classification Problems , 1998 .
[69] Hung Son Nguyen,et al. A method of Web search result clustering based on rough sets , 2005, The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05).
[70] Lotfi A. Zadeh,et al. Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..
[71] Hung Son Nguyen,et al. Rough Set Data Analysis in the KDD Process , 2000 .
[72] Ryszard S. Michalski,et al. Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments , 1994, Machine Learning.
[73] Hung Son Nguyen,et al. Analysis of STULONG Data by Rough Set Exploration System (RSES) , 2003 .
[74] Archie Blake. Canonical expressions in Boolean algebra , 1938 .
[75] Hiroshi Motoda,et al. Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.
[76] Andrzej Skowron,et al. Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables , 1994, ISMIS.
[77] Jiawei Han,et al. Data Mining: Concepts and Techniques , 2000 .
[78] Andrzej Skowron,et al. New Directions in Rough Sets, Data Mining, and Granular-Soft Computing , 1999, Lecture Notes in Computer Science.
[79] Ron Musick,et al. Scalable High Performance Computing for Knowledge Discovery and Data Mining , 1998, Springer US.
[80] Hung Son Nguyen,et al. A View on Rough Set Concept Approximations , 2003, Fundam. Informaticae.
[81] Willard Van Orman Quine,et al. The Problem of Simplifying Truth Functions , 1952 .
[82] Stefan Wrobel,et al. Machine Learning: ECML-95 , 1995, Lecture Notes in Computer Science.
[83] Andrzej Skowron,et al. Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems , 1998 .
[84] Wojciech Ziarko,et al. The Discovery, Analysis, and Representation of Data Dependencies in Databases , 1991, Knowledge Discovery in Databases.
[85] Sinh Hoa Nguyen,et al. On Finding Optimal Discretizations for Two Attributes , 1998, Rough Sets and Current Trends in Computing.
[86] S. Tsumoto,et al. Rough set methods and applications: new developments in knowledge discovery in information systems , 2000 .
[87] Bart Selman,et al. Pushing the Envelope: Planning, Propositional Logic and Stochastic Search , 1996, AAAI/IAAI, Vol. 2.
[88] Jan Komorowski,et al. An Approach to Mining Data with Continuous Decision Values , 2005, Intelligent Information Systems.
[89] John Mingers,et al. An Empirical Comparison of Pruning Methods for Decision Tree Induction , 1989, Machine Learning.
[90] Matthew W. Moskewicz,et al. Cha : Engineering an e cient SAT solver , 2001, DAC 2001.
[91] Sinh Hoa Nguyen,et al. Rough Set Approach to Sunspot Classification Problem , 2005, RSFDGrC.
[92] Bart Selman,et al. Planning as Satisfiability , 1992, ECAI.
[93] Peter A. Flach,et al. Rule induction , 2003 .
[94] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[95] Jorma Rissanen,et al. MDL-Based Decision Tree Pruning , 1995, KDD.
[96] Andrzej Skowron,et al. Boolean Reasoning Scheme with Some Applications in Data Mining , 1999, PKDD.
[97] Hung Son Nguyen,et al. A Soft Decision Tree , 2002, Intelligent Information Systems.
[98] Hung Son NguyenInstitute. Tasks Decomposition Problem in Multi-agent Systems , 1998 .
[99] Andrzej Skowron,et al. Rough-Fuzzy Hybridization: A New Trend in Decision Making , 1999 .
[100] Gregory Piatetsky-Shapiro,et al. Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.
[101] Catherine Kord,et al. The Young Man , 1984 .
[102] George Markowsky,et al. On the number of prime implicants , 1978, Discret. Math..
[103] Christopher Umans. The Minimum Equivalent DNF Problem and Shortest Implicants , 2001, J. Comput. Syst. Sci..
[104] Z. Pawlak,et al. Rough sets perspective on data and knowledge , 2002 .
[105] Bart Selman,et al. Ten Challenges in Propositional Reasoning and Search , 1997, IJCAI.
[106] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.
[107] William Frawley,et al. Knowledge Discovery in Databases , 1991 .
[108] Nada Lavrac,et al. The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains , 1986, AAAI.
[109] Patrick Prosser,et al. HYBRID ALGORITHMS FOR THE CONSTRAINT SATISFACTION PROBLEM , 1993, Comput. Intell..
[110] Sharad Malik,et al. Chaff: engineering an efficient SAT solver , 2001, Proceedings of the 38th Design Automation Conference (IEEE Cat. No.01CH37232).
[111] Zdzislaw Pawlak,et al. Some Issues on Rough Sets , 2004, Trans. Rough Sets.
[112] Clara Pizzuti. Computing prime implicants by integer programming , 1996, Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence.
[113] Dominik Slezak,et al. Approximate Entropy Reducts , 2002, Fundam. Informaticae.
[114] Jan G. Bazan,et al. Rough set algorithms in classification problem , 2000 .
[115] David S. Johnson,et al. Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .
[116] Marco Richeldi,et al. Class-Driven Statistical Discretization of Continuous Attributes (Extended Abstract) , 1995, ECML.
[117] Dominik Ślęzak,et al. Various approaches to reasoning with frequency based decision reducts: a survey , 2000 .
[118] Jian Pei,et al. Mining frequent patterns without candidate generation , 2000, SIGMOD 2000.
[119] Jakub Wroblewski,et al. Theoretical Foundations of Order-Based Genetic Algorithms , 1996, Fundam. Informaticae.
[120] Rakesh Agrawal,et al. SPRINT: A Scalable Parallel Classifier for Data Mining , 1996, VLDB.
[121] Johann Eder,et al. Logic and Databases , 1992, Advanced Topics in Artificial Intelligence.
[122] D. Vanderpooten. Similarity Relation as a Basis for Rough Approximations , 1995 .
[123] W. Quine. On Cores and Prime Implicants of Truth Functions , 1959 .
[124] Renée J. Miller,et al. Very Large Databases , 1999 .
[125] Jerzy W. Grzymala-Busse,et al. Data mining and rough set theory , 2000, CACM.
[126] Simon Parsons,et al. Principles of Data Mining by David J. Hand, Heikki Mannila and Padhraic Smyth, MIT Press, 546 pp., £34.50, ISBN 0-262-08290-X , 2004, The Knowledge Engineering Review.
[127] Bernhard Pfahringer,et al. Compression-Based Discretization of Continuous Attributes , 1995, ICML.
[128] Mohamed Quafafou,et al. alpha-RST: a generalization of rough set theory , 2000, Inf. Sci..
[129] Dominik Slezak,et al. Approximate Reducts and Association Rules - Correspondence and Complexity Results , 1999, RSFDGrC.
[130] Simon Kasif,et al. A System for Induction of Oblique Decision Trees , 1994, J. Artif. Intell. Res..
[131] Claude E. Shannon,et al. A symbolic analysis of relay and switching circuits , 1938, Transactions of the American Institute of Electrical Engineers.
[132] Donald W. Loveland,et al. Automated theorem proving: a logical basis , 1978, Fundamental studies in computer science.
[133] Joel H. Saltz,et al. Decision Tree Construction for Data Mining on Cluster of Shared-Memory Multiprocessors , 2001 .
[134] Huan Liu,et al. Discretization: An Enabling Technique , 2002, Data Mining and Knowledge Discovery.
[135] Zbigniew Michalewicz,et al. Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.
[136] R. Keefe. Theories of vagueness , 2000 .
[137] Jerzy W. Grzymala-Busse,et al. Rough Sets , 1995, Commun. ACM.
[138] David J. Spiegelhalter,et al. Machine Learning, Neural and Statistical Classification , 2009 .
[139] B. R. Gaines,et al. Machine learning and uncertain reasoning , 1990 .
[140] Jan G. Bazan. Chapter 17 a Comparison of Dynamic and Non{dynamic Rough Set Methods for Extracting Laws from Decision Tables , 1998 .
[141] Jakub Wróblewski,et al. Genetic Algorithms in Decomposition and Classification Problems , 1998 .
[142] Robert C. Holte,et al. Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.
[143] Lawrence Ryan. Efficient algorithms for clause-learning SAT solvers , 2004 .
[144] D. J. Malcolme-Lawes,et al. If… Then….Else , 1969 .
[145] Toshinori Munakata,et al. Knowledge discovery , 1999, Commun. ACM.
[146] Pat Langley,et al. Static Versus Dynamic Sampling for Data Mining , 1996, KDD.
[147] Jerzy W. Grzymala-Busse,et al. Transactions on Rough Sets I , 2004, Lecture Notes in Computer Science.
[148] Andrzej Skowron,et al. The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.
[149] Hung Son Nguyen,et al. Discretization Problem for Rough Sets Methods , 1998, Rough Sets and Current Trends in Computing.
[150] Jerzy W. Grzymala-Busse. LERS - A Data Mining System , 2005, The Data Mining and Knowledge Discovery Handbook.
[151] Ron Kohavi,et al. Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.
[152] Hung Son Nguyen,et al. On the Decision Table with Maximal Number of Reducts , 2003, RSKD.
[153] Andrzej Skowron,et al. Rough-Neural Computing , 2004, Cognitive Technologies.
[154] Richard C. T. Lee,et al. Symbolic logic and mechanical theorem proving , 1973, Computer science classics.
[155] Slawomir T. Wierzchon,et al. Intelligent Information Systems 2002 , 2002 .
[156] Joseph R. Shoenfield,et al. Mathematical logic , 1967 .
[157] Lior Rokach,et al. Data Mining And Knowledge Discovery Handbook , 2005 .
[158] Sergiu Rudeanu. Boolean functions and equations , 1974 .
[159] Peter Clark,et al. The CN2 Induction Algorithm , 1989, Machine Learning.
[160] Zbigniew W. Ras,et al. Methodologies for Intelligent Systems , 1991, Lecture Notes in Computer Science.
[161] Zdzislaw Pawlak. Rough classification , 1999, Int. J. Hum. Comput. Stud..
[162] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[163] Hung Son Nguyen,et al. A Tolerance Rough Set Approach to Clustering Web Search Results , 2004, PKDD.
[164] Jack Minker,et al. Logic and Data Bases , 1978, Springer US.
[165] Lawrence J. Henschen,et al. What Is Automated Theorem Proving? , 1985, J. Autom. Reason..
[166] Donato Malerba,et al. A Comparative Analysis of Methods for Pruning Decision Trees , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[167] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[168] Jan M. Zytkow,et al. Handbook of Data Mining and Knowledge Discovery , 2002 .
[169] Michel Rigo,et al. Abstract numeration systems and tilings , 2005 .
[170] Hung Son Nguyen,et al. Granular Computing: a Rough Set Approach , 2001, Comput. Intell..
[171] Joao Marques-Silva,et al. GRASP-A new search algorithm for satisfiability , 1996, Proceedings of International Conference on Computer Aided Design.
[172] J. S. Walijewski,et al. Representation Theorem for Boolean Algebras , 1993 .
[173] Jan Komorowski,et al. Principles of Data Mining and Knowledge Discovery , 2001, Lecture Notes in Computer Science.
[174] Ian Witten,et al. Data Mining , 2000 .
[175] R. Słowiński. Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory , 1992 .
[176] Andrzej Skowron,et al. Decomposition of Task Specification Problems , 1999, ISMIS.
[177] William W. Cohen. Fast Effective Rule Induction , 1995, ICML.
[178] Andrzej Skowron,et al. Synthesis of Adaptive Decision Systems from Experimental Data , 1995, SCAI.
[179] Randy Kerber,et al. ChiMerge: Discretization of Numeric Attributes , 1992, AAAI.
[180] Sabine Van Huffel,et al. On the Design of a Web-Based Decision Support System for Brain Tumour Diagnosis Using Distributed Agents , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops.
[181] Gevarter,et al. Overview of Expert Systems , 1982 .
[182] Heikki Mannila,et al. Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.
[183] Sandip Sen,et al. Minimal cost set covering using probabilistic methods , 1993, SAC '93.
[184] Jerzy W. Grzymala-Busse,et al. Transactions on Rough Sets XII , 2010, Lecture Notes in Computer Science.
[185] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.
[186] Zdzislaw Pawlak,et al. Information systems theoretical foundations , 1981, Inf. Syst..
[187] Wolfgang Bibel,et al. Automated Theorem Proving , 1987, Artificial Intelligence / Künstliche Intelligenz.
[188] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .
[189] Ramakrishnan Srikant,et al. Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.
[190] Marzena Kryszkiewicz. Maintenance of Reducts in the Variable Precision Rough Set Model , 1997 .
[191] Hui Wang,et al. Pattern extraction method for text classification , 2000 .
[192] Tomasz Imielinski,et al. Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.
[193] Marzena Kryszkiewicz,et al. Towards Scalable Algorithms for Discovering Rough Set Reducts , 2004, Trans. Rough Sets.
[194] Peter Clark,et al. Rule Induction with CN2: Some Recent Improvements , 1991, EWSL.
[195] R. Goodstein. Boolean algebra , 1963 .
[196] Sanjay Ranka,et al. CLOUDS: A Decision Tree Classifier for Large Datasets , 1998, KDD.
[197] Alexis Tsoukiàs,et al. Incomplete Information Tables and Rough Classification , 2001, Comput. Intell..
[198] Jerzy W. Grzymala-Busse,et al. LERS-A System for Learning from Examples Based on Rough Sets , 1992, Intelligent Decision Support.
[199] Hung Son Nguyen,et al. Scalable Classification Method Based on Rough Sets , 2002, Rough Sets and Current Trends in Computing.
[200] Andrzej Skowron,et al. Tolerance Approximation Spaces , 1996, Fundam. Informaticae.
[201] L. Zadeh,et al. Fuzzy logic and the calculi of fuzzy rules, fuzzy graphs, and fuzzy probabilities , 1999 .
[202] Lech Polkowski,et al. Rough Sets in Knowledge Discovery 2 , 1998 .