Object–Parameter Approaches to Predicting Unknown Data in an Incomplete Fuzzy Soft Set

Rough Set Approaches to Rule Induction from Incomplete Data

In this paper we assume that data are presented in the form of decision tables, incomplete when some attribute values are missing. Two main cases of missing attribute values are considered: lost (the original value was erased) and "do not care" conditions (the original value was irrelevant). This paper uses, as the main tool, attribute-value pair blocks. These blocks are used to construct chara...

FUZZY SOFT SET THEORY AND ITS APPLICATIONS

In this work, we define a fuzzy soft set theory and its related properties. We then define fuzzy soft aggregation operator that allows constructing more efficient decision making method. Finally, we give an example which shows that the method can be successfully applied to many problems that contain uncertainties.

Predicting Suicide Attacks: A Fuzzy Soft Set Approach

This paper models a decision support system to predict the occurance of suicide attack in a given collection of cities. The system comprises two parts. First part analyzes and identifies the factors which affect the prediction. Admitting incomplete information and use of linguistic terms by experts, as two characteristic features of this peculiar prediction problem we exploit the Theory of Fuzz...

A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data

The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...

Possibility Fuzzy Soft Set