"Rule + Exception" Strategies for Knowledge Management and Discovery

A common practice of human learning and knowledge management is to use general rules, exception rules, and exceptions to rules. One of the crucial issues is to find a right mixture of them. For discovering this type of knowledge, we consider “rule + exception”, or rule-plus-exception, strategies. Results from psychology, expert systems, genetic algorithms, and machine learning and data mining are summarized and compared, and their implications to knowledge management and discovery are examined. The study motivates and establishes a basis for the design and implementation of new algorithms for the discovery of “rule + exception” type knowledge.

[1]  Raymond Reiter,et al.  A Logic for Default Reasoning , 1987, Artif. Intell..

[2]  Alan L. Rector.  Defaults, Context, and Knowledge: Alternatives for OWL-Indexed Knowledge Bases , 2004, Pacific Symposium on Biocomputing.

[3]  Yiyu Yao,et al.  Mining High Order Decision Rules , 2003 .

[4]  Min Zhao,et al.  Multilevel data summarization from information systems: a "rule + exception" approach , 2003, AI Commun..

[5]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[6]  Wynne Hsu,et al.  Intuitive Representation of Decision Trees Using General Rules and Exceptions , 2000, AAAI/IAAI.

[7]  Paul Compton,et al.  Knowledge in Context: A Strategy for Expert System Maintenance , 1990, Australian Joint Conference on Artificial Intelligence.

[8]  Ivan Bratko,et al.  Machine Learning and Data Mining; Methods and Applications , 1998 .

[9]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[10]  Fernando Berzal Galiano,et al.  Relational decomposition through partial functional dependencies , 2002, Data Knowl. Eng..

[11]  Raymond T. Ng,et al.  A Unified Notion of Outliers: Properties and Computation , 1997, KDD.

[12]  Jan Komorowski,et al.  Principles of Data Mining and Knowledge Discovery , 2001, Lecture Notes in Computer Science.

[13]  Peter A. Flach,et al.  Subgroup Discovery with CN2-SD , 2004, J. Mach. Learn. Res..

[14]  Jadzia Cendrowska,et al.  PRISM: An Algorithm for Inducing Modular Rules , 1987, Int. J. Man Mach. Stud..

[15]  Daniel Sánchez,et al.  ART: A Hybrid Classification Model , 2004, Machine Learning.

[16]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[17]  Paul Thagard,et al.  Induction: Processes Of Inference , 1989 .

[18]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[19]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .

[20]  R. Nosofsky,et al.  Rule-plus-exception model of classification learning. , 1994, Psychological review.

[21]  Shusaku Tsumoto,et al.  Foundations of Intelligent Systems, 15th International Symposium, ISMIS 2005, Saratoga Springs, NY, USA, May 25-28, 2005, Proceedings , 2005, ISMIS.

[22]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

[23]  Jue Wang,et al.  Rule+Exception Modeling Based on Rough Set Theory , 1998, Rough Sets and Current Trends in Computing.

[24]  Andrzej Skowron,et al.  A Rough Set Framework for Data Mining of Propositional Default Rules , 1996, ISMIS.

[25]  H. Motoda,et al.  Performance evaluation of fusing two different knowledge sources in Ripple Down Rules method , 2005, Proceedings of the 2005 International Conference on Active Media Technology, 2005. (AMT 2005)..

[26]  Yiyu Yao,et al.  Peculiarity Oriented Multidatabase Mining , 2003, IEEE Trans. Knowl. Data Eng..

[27]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[28]  S. Tsumoto,et al.  Rough Set Theory and Granular Computing , 2003 .

[29]  Brian R. Gaines,et al.  Induction of ripple-down rules applied to modeling large databases , 1995, Journal of Intelligent Information Systems.

[30]  Einoshin Suzuki,et al.  Autonomous Discovery of Reliable Exception Rules , 1997, KDD.

[31]  Jan M. Zytkow,et al.  Unified algorithm for undirected discovery of exception rules , 2005, Int. J. Intell. Syst..

[32]  Wynne Hsu,et al.  Multi-level organization and summarization of the discovered rules , 2000, KDD '00.

[33]  Wen-Lian Hsu,et al.  Exploiting knowledge representation in an intelligent tutoring system for English lexical errors , 2002, International Conference on Computers in Education, 2002. Proceedings..

[34]  Brian R. Gaines,et al.  The Trade-Off between Knowledge and Data in Knowledge Acquisition , 1991, Knowledge Discovery in Databases.

[35]  Sadaaki Miyamoto,et al.  Rough Sets and Current Trends in Computing , 2012, Lecture Notes in Computer Science.

[36]  Jan M. Zytkow,et al.  Unified Algorithm for Undirected Discovery of Execption Rules , 2000, PKDD.

[37]  Janusz Kacprzyk,et al.  A Softened Formulation of Inductive Learning and Its Use for Coronary Disease Data , 2005, ISMIS.