Knowledge discovery by application of rough set models

The amount of electronic data available is growing very fast and this explosive growth in databases has generated a need for new techniques and tools that can intelligently and automatically extract implicit, previously unknown, hidden and potentially useful information and knowledge from these data. These tools and techniques are the subject of the field of Knowledge Discovery in Databases. In this Chapter we discuss selected rough set based solutions to two main knowledge discovery problems, namely the description problem and the classification (prediction) problem.

[1]  U. Wybraniec-Skardowska.  On a generalization of approximation space , 1989 .

[2]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[3]  Jaroslaw Stepaniuk,et al.  Data Transformation and Rough Sets , 1998, PKDD.

[4]  Krzysztof Slowinski,et al.  Rough Classification of HSV Patients , 1992, Intelligent Decision Support.

[5]  Jaroslaw Stepaniuk,et al.  Approximation Spaces in Extensions of Rough Set Theory , 1998, Rough Sets and Current Trends in Computing.

[6]  Jerzy W. Grzymala-Busse,et al.  A New Version of the Rule Induction System LERS , 1997, Fundam. Informaticae.

[7]  Yiyu Yao,et al.  On Generalizing Pawlak Approximation Operators , 1998, Rough Sets and Current Trends in Computing.

[8]  Roberto Gemello,et al.  An Integrated Characterization and Discrimination Scheme to Improve Learning Efficiency in Large Data Sets , 1989, IJCAI.

[9]  Ronald Fagin,et al.  Reasoning about knowledge , 1995 .

[10]  Zdzisław Pawlak,et al.  Rough sets based decision algorithm for treatment of duodenal ulcer by HSV , 1987 .

[11]  A. Tversky.  Features of Similarity , 1977 .

[12]  Jaroslaw Stepaniuk,et al.  Optimizations of Rough Set Model , 1998, Fundam. Informaticae.

[13]  Cecylia Rauszer,et al.  Knowledge Representation Systems for Groups of Agents , 1994 .

[14]  Andrzej Skowron,et al.  Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems , 1998 .

[15]  Andrzej Skowron,et al.  Application of Modal Logics and Rough Sets for Classifying Objects , 1995, WOCFAI.

[16]  Marzena Kryszkiewicz.  Maintenance of Reducts in the Variable Precision Rough Set Model , 1997 .

[17]  Ivo Düntsch,et al.  Statistical evaluation of rough set dependency analysis , 1997, Int. J. Hum. Comput. Stud..

[18]  Andrzej Skowron,et al.  Tolerance Approximation Spaces , 1996, Fundam. Informaticae.

[19]  Yiyu Yao,et al.  Generalization of Rough Sets using Modal Logics , 1996, Intell. Autom. Soft Comput..

[20]  Slavka Bodjanova,et al.  Approximation of fuzzy concepts in decision making , 1997, Fuzzy Sets Syst..

[21]  Casimir A. Kulikowski,et al.  Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems , 1990 .

[22]  Andrzej Skowron,et al.  Synthesis of Decision Systems from Data Tables , 1997 .

[23]  Ibrahim Tentush,et al.  On minimal absorbent sets for some types of tolerance relations , 1995 .

[24]  Roman Slowinski,et al.  Rough Classification of Patients After Highly Selective Vagotomy for Duodenal Ulcer , 1986, Int. J. Man Mach. Stud..

[25]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[26]  Zbigniew W. Ras,et al.  Cooperative Knowledge-Based Systems , 1996, Intell. Autom. Soft Comput..

[27]  Jerzy Stefanowski,et al.  Multistage Rough Set Analysis of Therapeutic Experience with Acute Pancreatitis , 1998 .

[28]  Maciej Kandulski,et al.  Surgical Wound Infection - Conducive Factors and Their Mutual Dependencies , 1992, Intelligent Decision Support.

[29]  W. Zakowski.  ON A CONCEPT OF ROUGH SEIS , 1982 .

[30]  Saso Dzeroski,et al.  Learning Nonrecursive Definitions of Relations with LINUS , 1991, EWSL.

[31]  Hiroshi Tanaka,et al.  Machine Discovery of Functional Components of Proteins from Amino-Acid Sequences Based on Rough Sets and Change of Representation , 1996, Intell. Autom. Soft Comput..

[32]  Urszula Wybraniec-Skardowska,et al.  Generalized Rough Sets in Contextual Spaces , 1997 .

[33]  Hiroshi Tanaka,et al.  PRIMEROSE: PROBABILISTIC RULE INDUCTION METHOD BASED ON ROUGH SETS AND RESAMPLING METHODS , 1995, Comput. Intell..

[34]  Xiaohua Hu,et al.  Rough Sets Similarity-Based Learning from Databases , 1995, KDD.

[35]  J. Stepaniuk.  Approximation logic of programs , 1990 .

[36]  J. Stepaniuk.  Decision rules for decision tables , 1994 .

[37]  Urszula Wybraniec-Skardowska,et al.  Extensions and Intentions in the Ruogh Set Theory , 1998, Inf. Sci..

[38]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

[39]  Gianpiero Cattaneo,et al.  Generalized Rough Sets (Preclusivity Fuzzy-Intuitionistic (BZ) Lattices) , 1997, Stud Logica.

[40]  Andrzej Skowron,et al.  EXTRACTING LAWS FROM DECISION TABLES: A ROUGH SET APPROACH , 1995, Comput. Intell..

[41]  Andrzej Skoworon,et al.  Data Filtration: A Rough Set Approach , 1993 .

[42]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[43]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[44]  T. Ho,et al.  A Rough Set Approach to Information Retrieval , 1998 .

[45]  Ryszard S. Michalski,et al.  A Theory and Methodology of Inductive Learning , 1983, Artificial Intelligence.

[46]  Gianpiero Cattaneo,et al.  Fuzzy Extension of Rough Sets Theory , 1998, Rough Sets and Current Trends in Computing.

[47]  Andrzej Skowron,et al.  From the Rough Set Theory to the Evidence Theory , 1991 .

[48]  Dimiter Vakarelov.  Rough Polyadic modal logics , 1999, J. Appl. Non Class. Logics.

[49]  Emmanuelle Martienne,et al.  Vagueness and Data Reduction in Concept Learning , 1998, ECAI.

[50]  Nick Cercone,et al.  Applying Knowledge Discovery to Predict Water-Supply Consumption , 1997, IEEE Expert.

[51]  Daijin Kim,et al.  A Handwritten Numeral Character Classification Using Tolerant Rough Set , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[52]  J Stepaniuk,et al.  [Application of the rough set theory in the prognostication of the diabetic nephropathy prevalence. Preliminary communication]. , 1998, Endokrynologia, diabetologia i choroby przemiany materii wieku rozwojowego : organ Polskiego Towarzystwa Endokrynologow Dzieciecych.

[53]  Emmanuelle Martienne,et al.  Learning Logical Descriptions for Document Understanding: A Rough Sets-Based Approach , 1998, Rough Sets and Current Trends in Computing.

[54]  R. Słowiński.  A generalization of the indiscernibility relation for rough set analysis of quantitative information , 1992 .

[55]  James F. Peters,et al.  Approximate Time Rough Software Cost Decision System: multicriteria Decision-Making Approach , 1999, ISMIS.

[56]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[57]  David S. Johnson,et al.  Approximation algorithms for combinatorial problems , 1973, STOC.

[58]  Andrzej Skowron,et al.  Discovery of Data Patterns with Applications to Decomposition and Classification Problems , 1998 .

[59]  Lotfi A. Zadeh,et al.  Similarity relations and fuzzy orderings , 1971, Inf. Sci..

[60]  Andrzej Skowron,et al.  Rough Mereological Foundatins for Design, Analysis, Synthesis, and Control in Distributed Systems , 1998, Inf. Sci..

[61]  S. K. Michael Wong,et al.  Comparison of Rough-Set and Statistical Methods in Inductive Learning , 1986, Int. J. Man Mach. Stud..

[62]  Andrzej Skowron,et al.  Information granules: Towards foundations of granular computing , 2001 .

[63]  Jerzy Tyszkiewicz,et al.  Probabilistic properties of approximation problems , 1991 .

[64]  Jaroslaw Stepaniuk,et al.  Attribute Discovery and Rough Sets , 1997, PKDD.

[65]  Andrzej Czyzewski,et al.  Speaker-Independent Recognition of Digits - Experiments with Neural Networks, Fuzzy Logic and Rough Sets , 1996, Intell. Autom. Soft Comput..

[66]  Stephen D. Comer,et al.  An algebraic approach to the approximation of information , 1991, Fundamenta Informaticae.

[67]  Jerzy W. Grzymala-Busse,et al.  Machine learning for an expert system to predict preterm birth risk. , 1994, Journal of the American Medical Informatics Association : JAMIA.

[68]  Shusaku Tsumoto.  Formalization and Induction of Medical Expert System Rules Based on Rough Set Theory , 1998 .

[69]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[70]  Robert J. Schalkoff,et al.  Pattern recognition - statistical, structural and neural approaches , 1991 .

[71]  Nada Lavrac,et al.  A Relevancy Filter for Constructive Induction , 1998, IEEE Intell. Syst..

[72]  Shusaku Tsumoto.  Extraction of Experts' Decision Process from Clinical Databases Using Rough Set Model , 1997, PKDD.

[73]  J. A. Pomykala,et al.  On definability in the nondeterministic information system , 1988 .

[74]  S. Greco,et al.  Rough Approximation of a Preference Relation in a Pairwise Comparison Table , 1998 .

[75]  Andrzej Skowron,et al.  Searching for Relational Patterns in Data , 1997, PKDD.

[76]  Tsau Young Lin,et al.  A Review of Rough Set Models , 1997 .

[77]  Zdzisław Pawlak.  On rough relations , 1986 .

[78]  S. Marcus.  Tolerance rough sets, Čech topologies, learning processes , 1994 .

[79]  Zdzisław Pawlak,et al.  Rough sets and information systems , 1988 .

[80]  Salvatore Greco,et al.  Fuzzy Similarity Relation as a Basis for Rough Approximations , 1998, Rough Sets and Current Trends in Computing.

[81]  Dimiter Vakarelov,et al.  Information Systems, Similarity Relations and Modal Logics , 1998 .

[82]  Zbigniew W. Ras,et al.  Collaboration Control in Distributed Knowledge-Based Systems , 1997, Inf. Sci..

[83]  Jerzy Stefanowski,et al.  Rough Set Theory and Rule Induction Techniques for Discovery of Attribute Dependencies in Medical Information Systems , 1997, PKDD.

[84]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[85]  Jan Komorowski,et al.  Modelling cardiac patient set residuals using rough sets , 1997, AMIA.

[86]  Jaroslaw Stepaniuk.  Applications of finite models properties in approximation and algorithmic logics , 1991, Fundam. Informaticae.

[87]  J. Grzymala-Busse.  Managing uncertainty in expert systems , 1991 .

[88]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[89]  J. Stepaniuk.  Approximation Spaces, Reducts and Representatives , 1998 .

[90]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[91]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[92]  J. Kacprzyk,et al.  How different are social choice functions: a rough sets approach , 1996, Quality and Quantity.

[93]  David L. Waltz,et al.  Toward memory-based reasoning , 1986, CACM.

[94]  Zdzislaw Pawlak,et al.  On a problem concerning dependence spaces , 1992, Fundam. Informaticae.

[95]  Andrzej Skowron,et al.  Rough-Fuzzy Hybridization: A New Trend in Decision Making , 1999 .

[96]  Zbigniew Suraj,et al.  Discovery of Concurrent Data Models from Experimental Tables: A Rough Set Approach , 1995, Fundam. Informaticae.

[97]  R. Słowiński,et al.  Learning Decision Rules from Similarity Based Rough Approximations , 1998 .

[98]  A Wakulicz-Deja,et al.  Diagnose progressive encephalopathy applying the rough set theory. , 1997, International journal of medical informatics.

[99]  Andrzej Skowron,et al.  Towards a Rough Mereology-Based Logic for Approximate Solution Synthesis. Part 1 , 1997, Stud Logica.

[100]  Piero Pagliani,et al.  From Concept Lattices to Approximation Spaces: Algebraic Structures of Some Spaces of Partial Objects , 1993, Fundam. Informaticae.

[101]  T. Iwiński.  Algebraic approach to rough sets , 1987 .

[102]  Andrzej Skowron,et al.  Synthesis of Decision Rules for Object Classification , 1998 .

[103]  Ewa Orlowska.  Information Algebras , 1995, AMAST.

[104]  Ewa Orlowska,et al.  A logic of indiscernibility relations , 1984, Symposium on Computation Theory.

[105]  R. Słowiński,et al.  Rough sets approach to analysis of data from peritoneal lavage in acute pancreatitis. , 1988, Medical informatics = Medecine et informatique.

[106]  Dimiter Vakarelov,et al.  A model logic for similarity relations in pawlak knowledge representation systems , 1991, Fundam. Informaticae.

[107]  Yiyu Yao,et al.  An Analysis of Quantitative Measures Associated with Rules , 1999, PAKDD.

[108]  A. Skowron,et al.  Towards adaptive calculus of granules , 1998 .

[109]  Luís Torgo,et al.  Controlled Redundancy in Incremental Rule Learning , 1993, ECML.

[110]  Robert E. Kent,et al.  Rough Concept Analysis: A Synthesis of Rough Sets and Formal Concept Analysis , 1996, Fundam. Informaticae.

[111]  R. Słowiński,et al.  Rough sets analysis of diagnostic capacity of vibroacoustic symptoms , 1992 .

[112]  Jaroslaw Stepaniuk,et al.  Rough Set Data Mining of Diabetes Data , 1999, ISMIS.

[113]  Andrzej Skowron,et al.  Towards Discovery of Information Granules , 1999, PKDD.

[114]  I. Düntsch.  Rough Sets and Algebras of Relations , 1998 .

[115]  Beata Konikowska,et al.  A Logic for Reasoning about Similarity , 1998 .

[116]  Sinh Hoa Nguyen,et al.  Pattern Extraction from Data , 1998, Fundam. Informaticae.

[117]  J. Stepaniuk.  Elementary approximation theory , 1990 .

[118]  Jan Komorowski,et al.  Taming Large Rule Models in Rough Set Approaches , 1999, PKDD.

[119]  Andrzej Skowron,et al.  Rough mereology: A new paradigm for approximate reasoning , 1996, Int. J. Approx. Reason..

[120]  D. Dubois,et al.  Twofold fuzzy sets and rough sets—Some issues in knowledge representation , 1987 .

[121]  Ivo Düntsch,et al.  A Logic for Rough Sets , 1997, Theor. Comput. Sci..

[122]  P. Langley,et al.  Average-case analysis of a nearest neighbor algorthim , 1993, IJCAI 1993.

[123]  Wojciech Ziarko,et al.  Variable Precision Extension of Rough Sets , 1996, Fundam. Informaticae.