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Fuzzy associative matrix

About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.


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Book ChapterDOI
03 Sep 2001
TL;DR: This paper introduces a semantic model of fuzzy association rules which suggests to consider them as a convex combination of simple association rules and provides a sound theoretical basis and gives an explicit meaning to fuzzy associations.
Abstract: Fuzzy association rules provide a data mining tool which is especially interesting from a knowledge-representational point of view since fuzzy attribute values allow for expressing rules in terms of natural language. In this paper, we show that fuzzy associations can be interpreted in different ways and that the interpretation has a strong influence on their assessment and, hence, on the process of rule mining. We motivate the use of multiple-valued implication operators in order to model fuzzy association rules and propose quality measures suitable for this type of rule. Moreover, we introduce a semantic model of fuzzy association rules which suggests to consider them as a convex combination of simple association rules. This model provides a sound theoretical basis and gives an explicit meaning to fuzzy associations. Particularly, the aforementioned quality measures can be justified within this framework.

60 citations

Journal ArticleDOI
TL;DR: The developed computational method provides a better approach to overcome the drawback of existing high-order fuzzy time series models and its simplicity lies with the use of differences in consecutive values of various orders as forecasting parameter and a w-step fuzzy predictor in place of complicated computations of fuzzy logical relations.
Abstract: This paper presents a computational method of forecasting based on high-order fuzzy time series. The developed computational method provides a better approach to overcome the drawback of existing high-order fuzzy time series models. Its simplicity lies with the use of differences in consecutive values of various orders as forecasting parameter and a w-step fuzzy predictor in place of complicated computations of fuzzy logical relations. The objective of the present study is to examine the suitability of various high-order fuzzy time series models in forecasting. The general suitability of the developed method has been tested by implementing it in the forecasting of student enrollments of the University of Alabama and in the forecasting of crop (Lahi) production, a case of high uncertainty in time series data. The results obtained have been compared in terms of average error of forecast to show superiority of the proposed model.

60 citations

Journal ArticleDOI
TL;DR: The study of fuzziness in combinational switching systems by means of a suitable fuzzy algebra is discussed and a new technique for minimization of fuzzy functions is developed.
Abstract: The study of fuzziness in combinational switching systems by means of a suitable fuzzy algebra is discussed. The insufficiency of the methods for simplification of fuzzy functions as presented by Lee and Chang [9] and by Siy and Chen [10] is discussed. A new technique for minimization of fuzzy functions is developed. Special properties of fuzzy functions are discussed and their relationships to two-valued logic are investigated.

60 citations

09 May 1994
TL;DR: A Markoov fuzzy process is constructed, which represents transitions of grades of fuzzy sets, with a transition possibility measure and a general state space, to solve fuzzy dynamic programming with optimal stopping times and with general state spaces and action spaces under fuzzy transitions.
Abstract: Abstract This paper constructs a Markoov fuzzy process, which represents transitions of grades of fuzzy sets, with a transition possibility measure and a general state space. We analyse Snell's optimal stopping problem for the process and we apply the results to solve fuzzy dynamic programming with optimal stopping times and with general state spaces and action spaces under fuzzy transitions.

60 citations

Journal ArticleDOI
TL;DR: A new method to evaluate fuzzy linear regression models based on Tanaka's approach, where both input data and output data are fuzzy numbers, using T w -based fuzzy arithmetic operations is presented.

59 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20238
202216
20212
20201
20193
201825