Subgroup Discovery with Linguistic Rules

Subgroup discovery can be defined as a form of supervised inductive learning in which, given a population of individuals and a specific property of individuals in which we are interested, find population subgroups that have the most unusual distributional characteristics with respect to the property of interest. Subgroup discovery algorithms aim at discovering individual rules, which must be represented in explicit symbolic form and which must be simple and understandable in order to be recognized as actionable by potential users.

[1]  W. Klösgen.  Data mining tasks and methods: Subgroup discovery: deviation analysis , 2002 .

[2]  Bernadette Bouchon-Meunier,et al.  Fuzzy Prototypes: From a Cognitive View to a Machine Learning Principle , 2008, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models.

[3]  Chengqi Zhang,et al.  A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules , 2004, Inf. Sci..

[4]  Huan Liu,et al.  Book review: Machine Learning, Neural and Statistical Classification Edited by D. Michie, D.J. Spiegelhalter and C.C. Taylor (Ellis Horwood Limited, 1994) , 1996, SGAR.

[5]  J. Ross Quinlan,et al.  Generating Production Rules from Decision Trees , 1987, IJCAI.

[6]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[7]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[8]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[9]  Stefan Wrobel,et al.  An Algorithm for Multi-relational Discovery of Subgroups , 1997, PKDD.

[10]  Peter A. Flach,et al.  RSD: Relational Subgroup Discovery through First-Order Feature Construction , 2002, ILP.

[11]  Ian Horrocks,et al.  Combinations of Modal Logics , 2002, Artificial Intelligence Review.

[12]  Man Hon Wong,et al.  Mining fuzzy association rules in databases , 1998, SGMD.

[13]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[14]  Guoqing Chen,et al.  Fuzzy association rules and the extended mining algorithms , 2002, Inf. Sci..

[15]  Peter A. Flach,et al.  Database Dependency Discovery: A Machine Learning Approach , 1999, AI Commun..

[16]  Willi Klösgen,et al.  Census Data Mining – An Application , 2002 .

[17]  Eyke Hüllermeier,et al.  Fuzzy methods in machine learning and data mining: Status and prospects , 2005, Fuzzy Sets Syst..

[18]  Jan M. Zytkow,et al.  Handbook of Data Mining and Knowledge Discovery , 2002 .

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

[20]  Luc De Raedt,et al.  Clausal Discovery , 1997, Machine Learning.

[21]  Man Hon Wong,et al.  Finding Fuzzy Sets for the Mining of Fuzzy Association Rules for Numerical Attributes , 1998 .

[22]  María José del Jesús,et al.  Genetic learning of fuzzy rule-based classification systems cooperating with fuzzy reasoning methods , 1998, Int. J. Intell. Syst..

[23]  Peter A. Flach,et al.  Rule Evaluation Measures: A Unifying View , 1999, ILP.

[24]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[25]  María José del Jesús,et al.  Evolutionary Induction of Descriptive Rules in a Market Problem , 2005, Intelligent Data Mining.

[26]  Hisao Ishibuchi,et al.  Classification and modeling with linguistic information granules - advanced approaches to linguistic data mining , 2004, Advanced information processing.

[27]  Didier Dubois,et al.  On the representation, measurement, and discovery of fuzzy associations , 2005, IEEE Transactions on Fuzzy Systems.

[28]  María José del Jesús,et al.  Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing , 2007, IEEE Transactions on Fuzzy Systems.

[29]  Tzung-Pei Hong,et al.  Using divide-and-conquer GA strategy in fuzzy data mining , 2004, Proceedings. ISCC 2004. Ninth International Symposium on Computers And Communications (IEEE Cat. No.04TH8769).

[30]  Frank Puppe,et al.  Towards Knowledge-Intensive Subgroup Discovery , 2004, LWA.

[31]  E. Dahlman,et al.  A Critical Assessment of Benchmark Comparison in Planning , 2002, J. Artif. Intell. Res..

[32]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[33]  Branko Kavsek,et al.  APRIORI-SD: ADAPTING ASSOCIATION RULE LEARNING TO SUBGROUP DISCOVERY , 2006, IDA.

[34]  Gregory Piatetsky-Shapiro,et al.  The interestingness of deviations , 1994 .

[35]  Willi Klösgen,et al.  Explora: A Multipattern and Multistrategy Discovery Assistant , 1996, Advances in Knowledge Discovery and Data Mining.

[36]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[37]  Nada Lavrac,et al.  Active subgroup mining: a case study in coronary heart disease risk group detection , 2003, Artif. Intell. Medicine.

[38]  Keith C. C. Chan,et al.  An effective algorithm for discovering fuzzy rules in relational databases , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[39]  H. Ishibuchi.  Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases , 2004 .

[40]  Nada Lavrac,et al.  Expert-Guided Subgroup Discovery: Methodology and Application , 2011, J. Artif. Intell. Res..

[41]  Peter Clark,et al.  The CN2 induction algorithm , 2004, Machine Learning.

[42]  Tzung-Pei Hong,et al.  Fuzzy data mining for interesting generalized association rules , 2003, Fuzzy Sets Syst..