Evolutionary algorithms and fuzzy sets for discovering temporal rules

Abstract A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method’s ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.

[1]  Gregory Piatetsky-Shapiro,et al.  Knowledge Discovery in Real Databases: A Report on the IJCAI-89 Workshop , 1991, AI Mag..

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

[3]  Keith C. C. Chan,et al.  Mining fuzzy association rules , 1997, CIKM '97.

[4]  José Cristóbal Riquelme Santos,et al.  An evolutionary algorithm to discover numeric association rules , 2002, SAC '02.

[5]  Mario A. Góngora,et al.  Evolving temporal fuzzy itemsets from quantitative data with a multi-objective evolutionary algorithm , 2011, 2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS).

[6]  Francisco Herrera,et al.  A Multi-Objective Evolutionary Algorithm for Rule Selection and Tuning on Fuzzy Rule-Based Systems , 2007, 2007 IEEE International Fuzzy Systems Conference.

[7]  Adrian A. Hopgood,et al.  Intelligent Systems for Engineers and Scientists , 2021 .

[8]  Mario A. Góngora,et al.  Evolving Temporal Fuzzy Association Rules from Quantitative Data with a Multi-Objective Evolutionary Algorithm , 2011, HAIS.

[9]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[10]  Jesús Alcalá-Fdez,et al.  A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and Its Interaction With Rule Selection , 2007, IEEE Transactions on Fuzzy Systems.

[11]  P. S. Sastry,et al.  A survey of temporal data mining , 2006 .

[12]  Theophano Mitsa,et al.  Temporal Data Mining , 2010 .

[13]  Shashi Shekhar,et al.  Similarity-Profiled Temporal Association Mining , 2009, IEEE Transactions on Knowledge and Data Engineering.

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

[15]  Mehmet Kaya,et al.  MOGAMOD: Multi-objective genetic algorithm for motif discovery , 2009, Expert Syst. Appl..

[16]  Wan-Jui Lee,et al.  Discovery of fuzzy temporal association rules , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[17]  Renée J. Miller,et al.  Association rules over interval data , 1997, SIGMOD '97.

[18]  Kalyanmoy Deb,et al.  Multi-objective Optimization , 2014 .

[19]  Bhabesh Nath,et al.  Multi-objective rule mining using genetic algorithms , 2004, Inf. Sci..

[20]  Tzung-Pei Hong,et al.  An Overview of Mining Fuzzy Association Rules , 2008, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models.

[21]  Alicia Troncoso Lora,et al.  An evolutionary algorithm to discover quantitative association rules in multidimensional time series , 2011, Soft Comput..

[22]  Tzung-Pei Hong,et al.  Trade-off Between Computation Time and Number of Rules for Fuzzy Mining from Quantitative Data , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[23]  Francisco Herrera,et al.  Real-Coded Memetic Algorithms with Crossover Hill-Climbing , 2004, Evolutionary Computation.

[24]  Gilles Venturini,et al.  SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts , 1993, ECML.

[25]  Francisco Herrera,et al.  Fuzzy Sets and Their Extensions: Representation, Aggregation and Models , 2008 .

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

[27]  Sushil Jajodia,et al.  Discovering calendar-based temporal association rules , 2001, Proceedings Eighth International Symposium on Temporal Representation and Reasoning. TIME 2001.

[28]  Florent Masseglia,et al.  Discovering frequent behaviors: time is an essential element of the context , 2010, Knowledge and Information Systems.

[29]  Francisco Herrera,et al.  Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases , 2002, Advances in Fuzzy Systems - Applications and Theory.

[30]  Cheng-Hsiung Weng,et al.  Mining fuzzy specific rare itemsets for education data , 2011, Knowl. Based Syst..

[31]  Mario A. Góngora,et al.  Evolving Temporal Association Rules with Genetic Algorithms , 2010, SGAI Conf..

[32]  Reda Alhajj,et al.  Facilitating fuzzy association rules mining by using multi-objective genetic algorithms for automated clustering , 2003, Third IEEE International Conference on Data Mining.

[33]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[34]  Abdollah Homaifar,et al.  Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[35]  Roberto J. Bayardo,et al.  Mining the most interesting rules , 1999, KDD '99.

[36]  Dr. Alex A. Freitas.  Data Mining and Knowledge Discovery with Evolutionary Algorithms , 2002, Natural Computing Series.

[37]  Mekki Ksouri,et al.  A multivariable multiobjective predictive controller , 2013, Int. J. Appl. Math. Comput. Sci..

[38]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[39]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[40]  Sushil Jajodia,et al.  Discovering calendar-based temporal association rules , 2003 .

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

[42]  Graham Kendall,et al.  Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques , 2013 .

[43]  Gustavo Rossi,et al.  An approach to discovering temporal association rules , 2000, SAC '00.

[44]  Francisco Herrera,et al.  Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures , 2011, Inf. Sci..

[45]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[46]  María José del Jesús,et al.  NMEEF-SD: Non-dominated Multiobjective Evolutionary Algorithm for Extracting Fuzzy Rules in Subgroup Discovery , 2010, IEEE Transactions on Fuzzy Systems.

[47]  Jiawei Han,et al.  Mining Segment-Wise Periodic Patterns in Time-Related Databases , 1998, KDD.

[48]  Francisco Herrera,et al.  Genetic fuzzy systems: taxonomy, current research trends and prospects , 2008, Evol. Intell..

[49]  Oscar Cordón,et al.  Identification of linguistic fuzzy models by means of genetic algorithms , 1997 .

[50]  María José del Jesús,et al.  Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction , 2005, IEEE Transactions on Fuzzy Systems.

[51]  Akira Maeda,et al.  Data mining system using fuzzy rule induction , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[52]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[53]  Sridhar Ramaswamy,et al.  Cyclic association rules , 1998, Proceedings 14th International Conference on Data Engineering.

[54]  Tzung-Pei Hong,et al.  Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy , 2008, IEEE Transactions on Evolutionary Computation.

[55]  María José del Jesús,et al.  On the discovery of association rules by means of evolutionary algorithms , 2011, WIREs Data Mining Knowl. Discov..

[56]  Ute St. Clair,et al.  Fuzzy Set Theory: Foundations and Applications , 1997 .