PARTITION FUZZY DOMAIN WITH MULTI-GNANULARITY REPRESENTATION OF DATA BASING ON HEDGE ALGEBRA APPROACH

Nguyễn Tuấn Anh, Trần Thái Sơn
Author affiliations

Authors

  • Nguyễn Tuấn Anh Đại học Công nghệ thông tin & Truyên thông - Đại học Thái Nguyên
  • Trần Thái Sơn Viện công nghệ thông tin - Viện Hàn lâm khoa học và Công nghệ Việt Nam

DOI:

https://doi.org/10.15625/1813-9663/34/1/10797

Keywords:

Fuzzy association rule, algebra approach, multi-gnanularity, Data mining, membership functions

Abstract

The article presents methods of dividing quantitative attributes into fuzzy domainswith multi-gnanularity representation of data, basing on hedge algebra approach. With this approach,explored association rules will express more information, from general to specic knowledge, thanusing usual single-granularity representation of data. At the same time, as a consequence, the numberof exploring rules will be more, meeting the needs of the user.

Metrics

Metrics Loading ...

References

Ho N. C. and Long N. V., Fuzziness measure on complete hedges algebras and quantifying semantics of terms in linear hedge algebras, Fuzzy Sets and Systems, vol.158 (2007), 452-471.

Nguyen Cat Ho, Tran Thai Son, Tran Dinh Khang, Le Xuan Viet, Fuzziness Measure, Quantied Semantic Mapping And Interpolative Method of Approximate Reasoning in Medical Expert

Systems, Journal of Computer Science and Cybernetics, T.18(3)(2002), 237-252.

Cat Ho Nguyen, Witold Pedrycz, Thang Long Duong, Thai Son Tran, A genetic design of linguistic terms for fuzzy rule based classiers, International Journal of Approximate Reasoning 54 (2012),

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 Reasoning67 (2014), 244-262.

Giovanna Castellano, Anna Maria Fanelli, and Corrado Mencar, Fuzzy Information Granulation with Multiple Levels of Granularity, W. Pedrycz and S.-M. Chen (Eds.): Granular Computing and

Intell. Sys., ISRL 13, pp. 185202. Springer-Verlag Berlin Heidelberg 2011

Corrado Mencar, Marco Lucarelli, Ciro Castiello, Anna M. Fanelli. Design of Strong Fuzzy Partitions from Cuts. 8th Conference of the European Society for Fuzzy Logic and Technology

(EUSFLAT 2013)

Michela Antonelli, Pietro Ducange, Beatrice Lazzerini, Francesco Marcelloni, Multi-objective evolutionary design of granular rule-based classiers, Granul. Comput. (2016) 1:3758 DOI

1007/s41066-015-0004-z

M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni, Learning concurrently data and rule bases of Mamdani fuzzy rule-based systems by exploiting a novel interpretability index, Soft Computing

(2011) 15:19811998 DOI 10.1007/s00500-010-0629-4.

P.Pulkkinen and H.Koivisto. A Dynamically Constrained Multiobjective Genetic Fuzzy System for Regression Problems. IEEE Trans.on Fuzzy Systems. vol 18 No1, (2010)

J.Alcala-Fdes, R. Alcala and F.Herrera A Fuzzy Association Rule-Based Classication Model for High-Dimentional problems with Genetic Rule Selection and lateral Tuning. IEEE Tran. on

Fuzzy Systems. vol 19No5 (2011), 857-872.

M.J. Gacto, R. Alcal, F. Herrera, Interpretability of linguistic fuzzy rule-basedsystems: An overview of interpretability measures, Information Sciences 181 (2011) 43404360 doi:

1016/j.ins.2011.02.021.

Herrera, F., Martinez L., Learning the Membership Function Contexts for Mining Fuzzy Association Rules by Using Genetic Algorithms, Fuzzy Set and System 160 (2009), 905-921

C. Chen, T. Hong, Vincent S. T. and L. Chen, Multi-objective genetic-fuzzy data mining, Information and Control.Volume 8, Number 10(A), October 2012.

Dumidu Wijayasekara, Milos Manic, Data Driven Fuzzy Membership Function Generation for Increased Understandability, IEEE International Conference on Fuzzy Systems, July 2014,

DOI10.1109/FUZZ-IEEE.2014.6891547.

Yao, Y. Granular, A triarchic theory of granular computing, June 2016, Granular Computing, Volume 1, Isue 2, pp 145157, (2016) 1: 145. doi:10.1007/s41066-015-0011-0

Zadeh, Lot A. "The concept of a linguistic variable and its application to approximate reasoning-I." Information sciences 8.3 (1975): 199-249.

D. L. Olson, and D. Delen, "Advanced data mining techniques", Springer Science & Business Media, 2008.

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.

Downloads

Published

08-08-2018

How to Cite

[1]
N. T. Anh and T. T. Sơn, “PARTITION FUZZY DOMAIN WITH MULTI-GNANULARITY REPRESENTATION OF DATA BASING ON HEDGE ALGEBRA APPROACH”, JCC, vol. 34, no. 1, p. 63–76, Aug. 2018.

Issue

Section

Computer Science