HEDGES ALGEBRAS AND PROBLEM FUZZY PARTITION FOR QUALITATIVE ATTRIBUTES
Author affiliations
DOI:
https://doi.org/10.15625/1813-9663/32/4/9145Keywords:
Data mining, fuzzy association rules, genetic algorithms, membership functions, Hedge algebrasAbstract
The pager refers to the construction of sets of the membership functions (MFs), which partition quantitative attributes in database into optimal fuzzy domains for extracting fuzzy association rules in the direction of hedge algebras approach. Some advantages of this method is demonstrated through the analysis of experiments on one set of standard data.
Keywords. Data mining; fuzzy association rules; genetic algorithms; membership functions; Hedge
algebras
Metrics
References
L. Eshelman, The chc adaptive search algorithm: How to have safe serach when engaging in nontraditional genetic recombination, in: G. Rawlin (ed.), Foundations of Genetic Algorithms, 265–283 (1991)
Herrera, Martinez, Learning the Membership Function Contexts for Mining Fuzzy Association Rules by Using Genetic Algorithms, Fuzzy Set and System, 905-921 (2009)
Li-Xing Wang and J.M.Mendel, Generating Fuzzy Rules by Lerning from Examples, IEEE Trans. SMC, 1 (1992)
C. Chen, T. Hong, Vincent S. T. and L. Chen, Multi-objective genetic-fuzzy data mining. International Journal of Innovative Computing,
Information and Control, 8 (2012)
M.J. Gacto, R. Alcalá, F. Herrera, Interpretability of linguistic fuzzy rule-basedsystems: An overview of interpretability measures,
Information Sciences, 8 (2011)
M. Antonelli, P. Ducange, F. Marcelloni, Genetic Training Instance Selection in Multiobjective Evolutionary Fuzzy Systems: A coevolutionary Approach, IEEE Trans. on Fuzzy Systems, 20, 276-290 (2012)
J.Alcala-Fdes, R. Alcala and F.Herrera, A Fuzzy Association Rule-Based Classification Model for High-Dimentional problems with Genetic Rule Selection and lateral Tuning, IEEE Tran. on Fuzzy Systems, 19, 857-872 (2011)
Alcala-Fdez, Jes{'u}s and Alcala, Rafael and Herrera, Francisco, A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning, IEEE Transactions on Fuzzy Systems, 19, 5, 857-872 (2011)
P.Pulkkinen and H.Koivisto, A Dynamically Constrained Multiobjective Genetic Fuzzy System for Regression Problems, IEEE Tran. on Fuzzy Systems, 18, 857-872 (2010)
Corrado Mencar, Marco Lucarelli, Ciro Castiello, Anna M. Fanelli, Design of Strong Fuzzy Partitions from Cuts, Conference of the European Society for Fuzzy Logic and Technology, 2013
Tanaka, H, Uejima, S, and Asia, K., Linear regression analysis with Fuzzy model, IEEE Trans. Systems.Man.Cybernet, 12, 903-07 (1982)
Nguyen Cat Ho, Tran Thai Son, Duong Thang Long, Tiếp cận đại số gia tử cho phân lớp mờ, Journal of Computer Science and Cybernetics, 25, 53–68 (2009)
Cat Ho Nguyen, Thai Son Tran, Dinh Phong Pham, Modeling of a semantics core of linguistic terms based on an extensionof hedge algebra semantics and its application, International Journal of Approximate Reasoning, 244-262 (2014)
Nguyen, Cat Ho and Pedrycz, Witold and Duong, Thang Long and Tran, Thai Son, A genetic design of linguistic terms for fuzzy rule based classifiers, International Journal of Approximate Reasoning, 54, 1, 1-21 (2013)
Nguyen Cat Ho, Tran Thai Son, Fuzziness Measure, Quantified Semantic Mapping And Interpolative Method of Approximate Reasoning in Medical Expert Systems, Journal of Computer Science and Cybernetics, 18, 3, 237-252 (2002)
Ho, Nguyen Cat and Wechler, Wolfgang, Hedge algebras: an algebraic approach to structure of sets of linguistic truth values, Fuzzy sets and systems, 35, 3, 281-293 (1990)
Nguyen Cat Ho, Wechler, Wolfgang, Extended hedge algebras and their application to fuzzy logic, Fuzzy sets and systems, 52, 3, 259-281 (1992)
Jiawei Han, Data Mining: Concepts and Techniques.: University of Illinois at Urbana-Champaign, Micheline Kamber, 2012
R. Agrawal, T. Imielinski, A. Swami, Fast algorithms for mining association rules, The International Conference on Very Large Database,
-499 (1994)
Agrawal, Rakesh and Srikant, Ramakrishnan and others, Fast algorithms for mining association rules, Proc. 20th int. conf. very large data bases, VLDB, 1215, 487-499 (1994)
Olson, David L and Delen, Dursun, Advanced data mining techniques, 2008, Springer Science & Business Media
Chen, Chun-Hao and Hong, Tzung-Pei and Lee, Yeong-Chyi and Tseng, Vincent S, Finding Active Membership Functions for Genetic-Fuzzy Data Mining, International Journal of Information Technology & Decision Making, 14,06, 1215-1242 (2015).
Downloads
Published
How to Cite
Issue
Section
License
1. We hereby assign copyright of our article (the Work) in all forms of media, whether now known or hereafter developed, to the Journal of Computer Science and Cybernetics. We understand that the Journal of Computer Science and Cybernetics will act on my/our behalf to publish, reproduce, distribute and transmit the Work.2. This assignment of copyright to the Journal of Computer Science and Cybernetics is done so on the understanding that permission from the Journal of Computer Science and Cybernetics is not required for me/us to reproduce, republish or distribute copies of the Work in whole or in part. We will ensure that all such copies carry a notice of copyright ownership and reference to the original journal publication.
3. We warrant that the Work is our results and has not been published before in its current or a substantially similar form and is not under consideration for another publication, does not contain any unlawful statements and does not infringe any existing copyright.
4. We also warrant that We have obtained the necessary permission from the copyright holder/s to reproduce in the article any materials including tables, diagrams or photographs not owned by me/us.
