Capturing Digest Emotions by Means of Fuzzy Linguistic Aggregation

2016 ◽  
pp. 113-139 ◽  
Author(s):  
C. Brenga ◽  
A. Celotto ◽  
V. Loia ◽  
S. Senatore
Keyword(s):  
Fuzzy Linguistic

scholarly journals Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 370
Author(s):  
Shuangsheng Wu ◽  
Jie Lin ◽  
Zhenyu Zhang ◽  
Yushu Yang
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Agglomerative Hierarchical Clustering ◽  
Research Gaps ◽  
Judicial Practice ◽  
Linguistic Term ◽  
Clustering Effect ◽  
Hierarchical Clustering Algorithm ◽  
Fuzzy Linguistic

The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed clustering algorithms are applied in the field of judicial execution, which provides decision support for the executive judge to determine the focus of the investigation and the control. A clustering example verifies the clustering algorithm’s effectiveness in the context of hesitant fuzzy linguistic decision information.

USING COLLABORATIVE FILTERING FOR DEALING WITH MISSING VALUES IN NUCLEAR SAFEGUARDS EVALUATION

2010 ◽  
Vol 18 (04) ◽  
pp. 431-449 ◽  
Author(s):  
ROSA M. RODRÍGUEZ ◽  
LUIS MARTÍNEZ ◽  
DA RUAN ◽  
JUN LIU
Keyword(s):  
Collaborative Filtering ◽  
Missing Values ◽  
Evaluation Process ◽  
Nuclear Safeguards ◽  
Sources Of Information ◽  
Energy Agency ◽  
Trust Measure ◽  
Many Sources ◽  
Linguistic Approach ◽  
Fuzzy Linguistic

Nuclear safeguards evaluation aims to verify that countries are not misusing nuclear programs for nuclear weapons purposes. Experts of the International Atomic Energy Agency (IAEA) carry out an evaluation process in which several hundreds of indicators are assessed according to the information obtained from different sources, such as State declarations, on-site inspections, IAEA non-safeguards databases and other open sources. These assessments are synthesized in a hierarchical way to obtain a global assessment. Much information and many sources of information related to nuclear safeguards are vague, imprecise and ill-defined. The use of the fuzzy linguistic approach has provided good results to deal with such uncertainties in this type of problems. However, a new challenge on nuclear safeguards evaluation has attracted the attention of researchers. Due to the complexity and vagueness of the sources of information obtained by IAEA experts and the huge number of indicators involved in the problem, it is common that they cannot assess all of them appearing missing values in the evaluation, which can bias the nuclear safeguards results. This paper proposes a model based on collaborative filtering (CF) techniques to impute missing values and provides a trust measure that indicates the reliability of the nuclear safeguards evaluation with the imputed values.

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