Proceedings of the Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support

Knowledge discovery by Compensatory Fuzzy Logic predicates using a metaheuristic approach

Authors
Marlies Martínez Alonso, Rafael Espín Andrade
Corresponding Author
Marlies Martínez Alonso
Available Online October 2013.
DOI
https://doi.org/10.2991/.2013.3How to use a DOI?
Keywords
Compensatory Fuzzy Logic, Knowledge Discovery, Metaheuristic algorithms
Abstract
Compensatory Fuzzy Logic (CFL) is a logical system, which enables an optimal way for the modeling of knowledge. Its axiomatic character enables the work of natural language translation of logic, so it is used in knowledge discovery and decision-making. In this work we propose a general and flexible approach for knowledge discovery which allows obtaining different knowledge structure using a metaheuristic approach. The proposed method was tested by experimental analysis from a data set, using a tool developed in visual Prolog. The experimental results show some advantages of the proposed approach for representing patterns and trends from data.
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Proceedings
Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2013
ISBN
978-90-78677-86-4
ISSN
1951-6851
DOI
https://doi.org/10.2991/.2013.3How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Marlies Martínez Alonso
AU  - Rafael Espín Andrade
PY  - 2013/10
DA  - 2013/10
TI  - Knowledge discovery by Compensatory Fuzzy Logic predicates using a metaheuristic approach
BT  - Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support
PB  - Atlantis Press
SP  - 17
EP  - 26
SN  - 1951-6851
UR  - https://doi.org/10.2991/.2013.3
DO  - https://doi.org/10.2991/.2013.3
ID  - MartínezAlonso2013/10
ER  -