A Survey of Fuzzy Data Mining Techniques

Data mining is very popular recently due to lots of analysis applications of big data. A well-known algorithm for mining association rules from transactions is the Apriori algorithm. Because transactions may include quantitative values, fuzzy sets which can be used to handle quantitative values are thus utilized to mine fuzzy association rules. Hence in this chapter, some useful fuzzy data mining techniques are introduced. Firstly, with the predefined membership functions, the Apriori-based fuzzy data mining algorithms that provide an easily way to mine fuzzy association rules are described. Since they may be time-consuming when dataset size is large, several tree-based fuzzy data mining methods are then stated to improve the mining efficiency. Besides, how to define appropriate membership functions for fuzzy data mining is important and it can be transferred into an optimization problem. Four types of genetic-fuzzy mining approaches are thus given to find both membership functions and fuzzy association rules. At last, some extended issues are discussed to provide future research directions.

[1]  Tzung-Pei Hong,et al.  A fuzzy AprioriTid mining algorithm with reduced computational time , 2004, Appl. Soft Comput..

[2]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[3]  Tzung-Pei Hong,et al.  An effective parallel approach for genetic-fuzzy data mining , 2014, Expert Syst. Appl..

[4]  Tzung-Pei Hong,et al.  Mining association rules from quantitative data , 1999, Intell. Data Anal..

[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]  Tzung-Pei Hong,et al.  Mining Fuzzy Association Rules with Multiple Minimum Supports Using Maximum Constraints , 2004, KES.

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

[8]  Daniel Sánchez,et al.  Fuzzy association rules: general model and applications , 2003, IEEE Trans. Fuzzy Syst..

[9]  Tzung-Pei Hong,et al.  A multi-objective genetic-fuzzy mining algorithm , 2008, 2008 IEEE International Conference on Granular Computing.

[10]  Tzung-Pei Hong,et al.  Linguistic data mining with fuzzy FP-trees , 2010, Expert Syst. Appl..

[11]  Tzung-Pei Hong,et al.  A CMFFP-tree algorithm to mine complete multiple fuzzy frequent itemsets , 2015, Appl. Soft Comput..

[12]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[13]  Reda Alhajj,et al.  Genetic algorithm based framework for mining fuzzy association rules , 2005, Fuzzy Sets Syst..

[14]  Tzung-Pei Hong,et al.  THE MFFP‐TREE FUZZY MINING ALGORITHM TO DISCOVER COMPLETE LINGUISTIC FREQUENT ITEMSETS , 2014, Comput. Intell..

[15]  Toshihiko Watanabe,et al.  Mining fuzzy association rules of specified output field , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[16]  Stergios Papadimitriou,et al.  The fuzzy frequent pattern Tree , 2005 .

[17]  Yin-Fu Huang,et al.  A fuzzy approach for mining high utility quantitative itemsets , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[18]  Francisco-Javier Lopez,et al.  Extracting Biological Knowledge by Fuzzy Association Rule Mining , 2007, 2007 IEEE International Fuzzy Systems Conference.

[19]  Tzung-Pei Hong,et al.  GENETIC-FUZZY MINING WITH TAXONOMY , 2012 .

[20]  Tzung-Pei Hong,et al.  Finding Pareto-front Membership Functions in Fuzzy Data Mining , 2012, Int. J. Comput. Intell. Syst..

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

[22]  Weimin Ouyang,et al.  Mining Direct and Indirect Fuzzy Multiple Level Sequential Patterns in Large Transaction Databases , 2008, ICIC.

[23]  Mario A. Góngora,et al.  Web usage mining with evolutionary extraction of temporal fuzzy association rules , 2013, Knowl. Based Syst..

[24]  Tzung-Pei Hong,et al.  A SPEA2-based genetic-fuzzy mining algorithm , 2010, International Conference on Fuzzy Systems.

[25]  Reda Alhajj,et al.  Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining , 2008, Journal of Intelligent Information Systems.

[26]  Ramakrishnan Srikant,et al.  Mining Association Rules with Item Constraints , 1997, KDD.

[27]  Tzung-Pei Hong,et al.  Mining from quantitative data with linguistic minimum supports and confidences , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[28]  Tzung-Pei Hong,et al.  Multi-level fuzzy mining with multiple minimum supports , 2008, Expert Syst. Appl..

[29]  Tzung-Pei Hong,et al.  An Efficient Tree-based Fuzzy Data Mining Approach , 2010 .

[30]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[31]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

[32]  Tzung-Pei Hong,et al.  Incremental multiple fuzzy frequent pattern tree , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[33]  Francisco Herrera,et al.  Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms , 2009, Fuzzy Sets Syst..

[34]  Tzung-Pei Hong,et al.  A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions , 2006, Soft Comput..

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

[36]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

[37]  Tzung-Pei Hong,et al.  An Integrated MFFP-tree Algorithm for Mining Global Fuzzy Rules from Distributed Databases , 2013, J. Univers. Comput. Sci..

[38]  Tzung-Pei Hong,et al.  A Cluster-Based Genetic-Fuzzy Mining Approach for Items with Multiple Minimum Supports , 2008, PAKDD.

[39]  Zaheer Uddin,et al.  A Simple Technique for Generation and Minimization of Fuzzy Rules , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[40]  Keon-Myung Lee.  Mining generalized fuzzy quantitative association rules with fuzzy generalization hierarchies , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[41]  Tzung-Pei Hong,et al.  Maintenance algorithm for updating the discovered multiple fuzzy frequent itemsets for transaction deletion , 2014, 2014 International Conference on Machine Learning and Cybernetics.

[42]  Qiang Yang,et al.  Mining high utility itemsets , 2003, Third IEEE International Conference on Data Mining.

[43]  Tzung-Pei Hong,et al.  A modified approach to speed up genetic-fuzzy data mining with divide-and-conquer strategy , 2007, 2007 IEEE Congress on Evolutionary Computation.

[44]  Tzung-Pei Hong,et al.  Mining association rules with multiple minimum supports using maximum constraints , 2005, Int. J. Approx. Reason..

[45]  Wynne Hsu,et al.  Mining association rules with multiple minimum supports , 1999, KDD '99.

[46]  Cezary Z. Janikow,et al.  Fuzzy decision trees: issues and methods , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[47]  Louis Wehenkel,et al.  A complete fuzzy decision tree technique , 2003, Fuzzy Sets Syst..

[48]  Reda Alhajj,et al.  Genetic algorithms based optimization of membership functions for fuzzy weighted association rules mining , 2004, Proceedings. ISCC 2004. Ninth International Symposium on Computers And Communications (IEEE Cat. No.04TH8769).

[49]  Tzung-Pei Hong,et al.  An improved approach to find membership functions and multiple minimum supports in fuzzy data mining , 2009, Expert Syst. Appl..

[50]  Keith C. C. Chan,et al.  Mining fuzzy association rules in a bank-account database , 2003, IEEE Trans. Fuzzy Syst..

[51]  M. Shaw,et al.  Induction of fuzzy decision trees , 1995 .

[52]  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.

[53]  Tzung-Pei Hong,et al.  Mining Fuzzy Multiple-Level Association Rules from Quantitative Data , 2004, Applied Intelligence.

[54]  Daming Shi,et al.  Mining fuzzy association rules with weighted items , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[55]  Mario A. Góngora,et al.  Temporal fuzzy association rule mining with 2-tuple linguistic representation , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[56]  Tzung-Pei Hong,et al.  A survey of fuzzy web mining , 2013, WIREs Data Mining Knowl. Discov..

[57]  Pratima Gautam,et al.  A Model for Mining Multilevel Fuzzy Association Rule in Database , 2010, ArXiv.

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

[59]  Tzung-Pei Hong,et al.  A genetic-fuzzy mining approach for items with multiple minimum supports , 2007, 2007 IEEE International Fuzzy Systems Conference.

[60]  Weimin Ouyang,et al.  Mining Direct and Indirect Weighted Fuzzy Association Rules in Large Transaction Databases , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[61]  Sattar Hashemi,et al.  A New Algorithm for Mining Fuzzy Association Rules in the Large Databases Based on Ontology , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[62]  Vikram Pudi,et al.  Fuzzy association rule mining algorithm for fast and efficient performance on very large datasets , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[63]  Chung-Leung Lui,et al.  Discovery of Generalized Association Rules with Multiple Minimum Supports , 2000, PKDD.

[64]  Tzung-Pei Hong,et al.  A Cluster-Based Fuzzy-Genetic Mining Approach for Association Rules and Membership Functions , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[65]  Tzung-Pei Hong,et al.  Mining fuzzy frequent itemsets based on UBFFP trees , 2014, J. Intell. Fuzzy Syst..

[66]  Alexandre Parodi,et al.  A New Approach to Fuzzy Classifier Systems , 1993, ICGA.

[67]  Sadok Ben Yahia,et al.  Extracting compact and information lossless set of fuzzy association rules , 2007, 2007 IEEE International Fuzzy Systems Conference.

[68]  Tzung-Pei Hong,et al.  Mining Multiple-Level Association Rules Under the Maximum Constraint of Multiple Minimum Supports , 2006, IEA/AIE.

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

[70]  V. S. Ananthanarayana,et al.  Extraction and optimization of fuzzy association rules using multi-objective genetic algorithm , 2008, Pattern Analysis and Applications.

[71]  Attila Gyenesei,et al.  A Fuzzy Approach for Mining Quantitative Association Rules , 2000, Acta Cybern..

[72]  Tzung-Pei Hong,et al.  Fuzzy utility mining with upper-bound measure , 2015, Appl. Soft Comput..

[73]  Mehmet Kaya,et al.  Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules , 2006, Soft Comput..

[74]  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).

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

[76]  Tzung-Pei Hong,et al.  MOGA-based fuzzy data mining with taxonomy , 2013, Knowl. Based Syst..

[77]  Reda Alhajj,et al.  Effective Mining of Fuzzy Multi-Cross-Level Weighted Association Rules , 2006, ISMIS.

[78]  Jiawei Han,et al.  Discovery of Multiple-Level Association Rules from Large Databases , 1995, VLDB.

[79]  Wen-Yang Lin,et al.  Efficient mining of generalized association rules with non-uniform minimum support , 2007, Data Knowl. Eng..

[80]  Tzung-Pei Hong,et al.  Genetic-fuzzy mining with multiple minimum supports based on fuzzy clustering , 2011, Soft Comput..