Detection of Fuzzy Association Rules by Fuzzy Transforms

We present a new method based on the use of fuzzy transforms for detecting coarse-grained association rules in the datasets. The fuzzy association rules are represented in the form of linguistic expressions and we introduce a pre-processing phase to determine the optimal fuzzy partition of the domains of the quantitative attributes. In the extraction of the fuzzy association rules we use the AprioriGen algorithm and a confidence index calculated via the inverse fuzzy transform. Our method is applied to datasets of the 2001 census database of the district of Naples (Italy); the results show that the extracted fuzzy association rules provide a correct coarse-grained view of the data association rule set.

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

[2]  Petr Hájek,et al.  Mechanizing Hypothesis Formation , 1978 .

[3]  Attila Gyenesei,et al.  Multidimensional fuzzy partitioning of attribute ranges for mining quantitative data: Research Articles , 2004 .

[4]  Sankar K. Pal,et al.  Data mining in soft computing framework: a survey , 2002, IEEE Trans. Neural Networks.

[5]  Bernard De Baets,et al.  Fuzzy transforms of monotone functions with application to image compression , 2010, Inf. Sci..

[6]  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..

[7]  Susumu Horiguchi,et al.  Mining Quantitative and Fuzzy Association Rules , 2005 .

[8]  Vilém Novák,et al.  Fuzzy transform in the analysis of data , 2008, Int. J. Approx. Reason..

[9]  Vilém Novák,et al.  Analysis and prediction of time series using fuzzy transform , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

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

[11]  Vilém Novák,et al.  A comprehensive theory of trichotomous evaluative linguistic expressions , 2008, Fuzzy Sets Syst..

[12]  Salvatore Sessa,et al.  Fuzzy transforms method and attribute dependency in data analysis , 2010, Inf. Sci..

[13]  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.

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

[15]  Miguel Delgado,et al.  Mining Fuzzy Association Rules: An Overview , 2005 .

[16]  Salvatore Sessa,et al.  Fuzzy transforms for compression and decompression of color videos , 2010, Inf. Sci..

[17]  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).

[18]  Wanneng Shu,et al.  ECOGA Efficient Data Mining Approach for Fuzzy Association Rules , 2011, J. Softw..

[19]  W. Pedrycz,et al.  Fuzzy computing for data mining , 1999, Proc. IEEE.

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

[21]  Vilém Novák,et al.  Mining pure linguistic associations from numerical data , 2008, Int. J. Approx. Reason..

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

[23]  Chris Cornelis,et al.  Fuzzy Association Rules: a Two-Sided Approach , 2003 .

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

[25]  Etienne Kerre,et al.  Fuzzy Data Mining: Discovery of Fuzzy Generalized Association Rules+ , 2000 .

[26]  Salvatore Sessa,et al.  Compression and decompression of images with discrete fuzzy transforms , 2007, Inf. Sci..

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

[28]  Salvatore Sessa,et al.  Fuzzy transforms method in prediction data analysis , 2011, Fuzzy Sets Syst..

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

[30]  Irina Perfilieva,et al.  Fuzzy transforms: Theory and applications , 2006, Fuzzy Sets Syst..

[31]  Salvatore Sessa,et al.  An image coding/decoding method based on direct and inverse fuzzy transforms , 2008, Int. J. Approx. Reason..

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

[33]  Reda Alhajj,et al.  Efficient Automated Mining of Fuzzy Association Rules , 2002, DEXA.

[34]  Eyke Hüllermeier,et al.  A systematic approach to the assessment of fuzzy association rules , 2006, Data Mining and Knowledge Discovery.

[35]  M. K. Luhandjula.  Studies in Fuzziness and Soft Computing , 2013 .

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

[37]  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.

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

[39]  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.

[40]  T. Havránek,et al.  Mechanizing Hypothesis Formation: Mathematical Foundations for a General Theory , 1978 .

[41]  Attila Gyenesei,et al.  Multidimensional fuzzy partitioning of attribute ranges for mining quantitative data , 2004, Int. J. Intell. Syst..

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