Determining the harmonic contributions of multiple harmonic sources using data clustering analysis

2015 ◽  
Author(s):  
Jianwei Yang ◽  
Yuan Yang ◽  
Jing Chen ◽  
Ling Fu ◽  
Zhengyou He
Keyword(s):  
Clustering Analysis ◽  
Data Clustering ◽  
Using Data

Assessing the Job Satisfaction of Registered Nurses Using Sentiment Analysis and Clustering Analysis

2021 ◽  
pp. 107755872110352
Author(s):  
Matthew Jura ◽  
Joanne Spetz ◽  
Der-Ming Liou
Keyword(s):  
Job Satisfaction ◽  
Registered Nurses ◽  
Sentiment Analysis ◽  
Support Services ◽  
Clustering Analysis ◽  
Multivariate Analyses ◽  
Free Text ◽  
Registered Nursing ◽  
High Satisfaction ◽  
Using Data

Job satisfaction is a critical component of the professional work environment and is often ascertained through surveys that include structured or open-ended questions. Using data from 24,543 respondents to California Board of Registered Nursing biennial surveys, this study examines the job satisfaction of registered nurses (RNs) by applying clustering analysis to structured job satisfaction items and sentiment analysis to free-text comments. The clustering analysis identified three job satisfaction groups (low, medium, and high satisfaction). Sentiment analysis scores were significantly associated with the job satisfaction groups in both bivariate and multivariate analyses. Differences between the job satisfaction clusters were mostly driven by satisfaction with workload, adequacy of the clerical support services, adequacy of the number of RN staff, and skills of RN colleagues. In addition, there was dispersion in satisfaction related to involvement in management and policy decisions, recognition for a job well done, and opportunities for professional development.

scholarly journals Data clustering analysis of early reflections in small room

2018 ◽  
Vol 144 (4) ◽  
pp. EL328-EL332 ◽  
Author(s):  
Zhaoqi Zhang ◽  
Ge Zhu ◽  
Yong Shen
Keyword(s):  
Clustering Analysis ◽  
Data Clustering ◽  
Small Room

scholarly journals Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hong Li ◽  
Yuantao Xie ◽  
Juan Yang ◽  
Di Wang
Keyword(s):  
Panel Data ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Semiparametric Estimation ◽  
Similarity Measurement ◽  
Model Estimation ◽  
Similarity Coefficients ◽  
Clustering Model ◽  
Compound Model

This paper proposed a panel data clustering model based on D-vine and C-vine and supported a semiparametric estimation for parameters. These models include a two-step inference function for margins, two-step semiparameter estimation, and stepwise semiparametric estimation. In similarity measurement, similarity coefficients are constructed by a multivariate Hierarchical Nested Archimedean Copula (HNAC) model and compound PCC models, which are HNAC and D-vine compound model and HNAC and C-vine compound model. Estimation solutions and models evaluation are given for these models. In the case study, the clustering results of HNAC and D-vine compound model and HNAC and C-vine compound model are given, and the effect of different copula families on clustering results is also discussed. The result shows the models are effective and useful.

Operating Point Estimation for an Absorption Process using Data Clustering Technique

2013 ◽  
Vol 13 (3) ◽  
pp. 377-384
Author(s):  
M. Amin A. Majid ◽  
Shaharin A. Sulaima ◽  
Hamdan Mokhtar ◽  
A.L. Tamiru
Keyword(s):  
Data Clustering ◽  
Point Estimation ◽  
Operating Point ◽  
Absorption Process ◽  
Clustering Technique ◽  
Using Data
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