Identification and analysis of employee branding typology using fuzzy c-means clustering

2017 ◽  
Vol 24 (5) ◽  
pp. 1253-1268
Author(s):  
Thamaraiselvan Natarajan ◽  
Sridevi Periaiya ◽  
Senthil Arasu Balasubramaniam ◽  
Thushara Srinivasan
Keyword(s):  
Psychological Contract ◽  
Training And Development ◽  
Cluster Number ◽  
Content Type ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Level Of Knowledge ◽  
High Level

Purpose The purpose of this paper is to identify and analyse the typology of employee branding in an airline company using fuzzy c-means (FCM) clustering to improve the quality of employee brand (EB). Design/methodology/approach Data were collected from employees of Air India, Chennai division, using a questionnaire and analysed using FCM to find the optimum cluster number. The nature of each cluster was analysed to know its type. Findings The results prove the presence of four types of EB, namely, all-stars, injured reserves, rookies and strike-out kings in the aviation company. It is proven that employees in all-star have high level of knowledge of the desired brand (KDB) and psychological contract (PC), those in injured reserves have high KDB and low PC, rookies have low KDB and high PC and strike-out kings have low KDB and PC. Research limitations/implications The results of this study are limited to the Air India employees. This study contributes to employee branding by empirically substantiating the proposed typology using FCM. It proposes the need to analyse organisations individually before comparisons. Practical implications The management must focus on the quality of training and development programmes to enhance the position of rookies and strike-out kings. It must also receive regular feedback from injured reserves and strike-out kings to evaluate their perception of PC. Originality/value This is the first paper to empirically prove the typology of employee branding and to implement FCM in clustering employees for enhancing the EB’s quality.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

scholarly journals A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chunhua Ren ◽  
Linfu Sun
Keyword(s):  
Clustering Algorithms ◽  
Real Data ◽  
Local Information ◽  
Data Sets ◽  
Clustering Ensemble ◽  
K Nearest Neighbors ◽  
Fuzzy C Means ◽  
Clustering Quality ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

AbstractThe classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

Nurses' knowledge and attitude towards COVID-19 in the context of the acute health care settings in Jordan

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sajidah Alhwamdih ◽  
Hamzeh Y. Abunab ◽  
Abdullah Ahmad Algunmeeyn ◽  
Imad Alfayoumi ◽  
Sana Hawamdeh
Keyword(s):  
Health Care ◽  
Acute Care ◽  
Health Care Facilities ◽  
Content Type ◽  
Increased Risk ◽  
Level Of Knowledge ◽  
Acute Care Settings ◽  
Knowledge And Attitude ◽  
High Level ◽  
Nurses Knowledge

Purpose Nurses are at the front line in facing the COVID-19 outbreak and are at increased risk of becoming infected and might be the source of transmission in health-care facilities and the community. The purpose of this study is to assess the knowledge and attitude toward COVID1-19 among nurses in acute care settings in Jordan. This is expected to help with the global initiative to combat the COVID-19 epidemic. Design/methodology/approach A cross-sectional design was used to survey nurses' knowledge and attitude of COVID-19 among Jordanian nurses working in acute care settings. Findings The grand mean of knowledge items response was 8.94, implying that respondents possessed a high level of knowledge. The overall attitude score was positive for the participants, with a mean score of 5.93. Moreover, the results showed a significant relationship between knowledge and attitude scores. Originality/value The findings suggest that nurses in Jordan showed a high level of knowledge and a positive attitude toward COVID-19 during the outbreak's rapid rise period. This study showed specific aspects of knowledge and attitudes that should be focused on in future awareness and educational programs to promote all preventive and safety measures of COVID-19.

Emotional intelligence competencies in engineer’s effectiveness and engagement

2017 ◽  
Vol 22 (1) ◽  
pp. 70-86 ◽  
Author(s):  
Richard Boyatzis ◽  
Kylie Rochford ◽  
Kevin V. Cavanagh
Keyword(s):  
Social Intelligence ◽  
Explanatory Power ◽  
Corporate Training ◽  
Training And Development ◽  
Shared Vision ◽  
Mental Ability ◽  
Content Type ◽  
General Mental Ability ◽  
Source Data

Purpose Little research has explored the importance of interpersonal skills, and more specifically, emotional and social intelligence (ESI) competencies for an engineer’s effectiveness or engagement. Furthermore, to the knowledge, no studies have explored the explanatory power of ESI over and above general mental ability and personality for engineers. The paper aims to discuss these issues. Design/methodology/approach In this study the authors gathered multi-source data for 40 engineers in a multi-national manufacturing company. Findings The authors found that ESI as observed by their peers significantly predicted engineer effectiveness (ΔR2=0.313), while general mental ability (g) and personality did not. In the same study, an engineer’s engagement in their work was significantly predicted by the degree of shared vision within their teams, while g, personality and ESI did not predict engagement. Research limitations/implications The authors explore the implications of the findings for corporate training and development, undergraduate education, and graduate education of engineers. Originality/value The authors draw on 30 years of longitudinal studies showing ESI and quality of relationships can be significantly improved with the appropriate pedagogy emphasizing the building of one’s vision, developmental approaches to ESI, developing a shared vision with others, and inspirational coaching.

BPSD and the challenges faced by nurses

2020 ◽  
Vol 24 (1) ◽  
pp. 39-48
Author(s):  
Shizuka Otsuka ◽  
Akiko Hamahata ◽  
Masaki Abe
Keyword(s):  
Quality Of Care ◽  
Design Methodology ◽  
Psychological Symptoms ◽  
Nursing Intervention ◽  
Content Type ◽  
Peer Reviews ◽  
Behavioural And Psychological Symptoms ◽  
Level Of Knowledge ◽  
Meta Analyses

Purpose The purpose of this paper is to provide an overview of published literature on behavioural and psychological symptoms of dementia (BPSD) nursing in Japan and to highlight challenges that need to be resolved. Design/methodology/approach The criteria for retrieval of literature were as follows: a BPSD study conducted by a nurse in Japan, and it must have been published. Papers without conference proceedings and peer reviews and literature without English titles and abstracts were excluded. The PRISMA (preferred reporting items for systematic reviews and meta-analyses) was referenced. Findings Based on the analysis of 20 studies meeting the criteria, nurses tended to manage BPSD when all three of the following were clearly defined: attempts to understand BPSD, the provision of nursing intervention to improve the quality of care and clarification of the perception of BPSD. There were eight studies that implemented surveys considered to be helpful for nurses to understand BPSD with the aim of clarifying the symptomatic factors, meaning of each behaviour, etc. In the eight studies, nurses directly coped with BPSD in various ways. Four studies reported on how nurses perceive the associated behaviours and symptoms of BPSD patients. Originality/value This study suggests that not only implementing interventions but also aiming at improving nurses’ understanding of BPSD and their level of knowledge are crucial to promote BPSD nursing in Japan.

The role of the psychological contract in the motivation of nurses

2018 ◽  
Vol 31 (1) ◽  
pp. 62-76 ◽  
Author(s):  
Emma Corder ◽  
Linda Ronnie
Keyword(s):  
Health Care ◽  
Psychological Contract ◽  
Structured Interviews ◽  
Content Type ◽  
Nursing Managers ◽  
Quality Of Patient Care ◽  
Management Perspective ◽  
Manager Support

Purpose Although private health care is regarded as providing a premium quality experience for both patients and staff alike, it is not without its daily challenges for health professionals. This study aims to explore the psychological contract of nurses to develop a greater understanding of how employee–employer interaction impacts motivation levels. Design/methodology/approach Data were gathered through semi-structured interviews with thirteen nurses at a private hospital in South Africa. Five nursing managers were interviewed to provide a management perspective. Thematic analysis was used to identify the salient elements of the psychological contract and to establish connections with motivational features. Findings The psychological contract of nurses was balanced in nature, contained predominantly relational elements and was characterized by the need for manager support, leadership and autonomy. Motivation was a by-product of fulfilment and was enhanced by a combination of tangible and intangible rewards. Practical implications Nursing managers should recognize their role in caring for the wellbeing of their staff and should be trained accordingly. Equipping nurses with the necessary tools to work autonomously, as well as acknowledging their skills, will stimulate confidence and improve motivation. Originality/value This study makes an important contribution to the existing literature on the psychological contract of nurses within the health-care system. It provides insight into relationship-based mechanisms that can be used to improve the motivation of nurses and thus impact the overall quality of patient care.

An Outlier Detection Method Based on Fuzzy C-Means Clustering

2009 ◽  
Vol 419-420 ◽  
pp. 165-168
Author(s):  
Qiang Li ◽  
Jian Pei Zhang ◽  
Guang Sheng Feng
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Detection Method ◽  
Experimental Results ◽  
Data Mining Techniques ◽  
Fuzzy C Means ◽  
Second Stage ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Two Stages

Both fuzzy c-means (FCM) clustering and outlier detection are useful data mining techniques in real applications. In this paper, we show that the task of outlier detection could be achieved as by-product of fuzzy c-means clustering. The proposed strategy consists of two stages. The first stage consists of purely fuzzy c-means process, while the second stage identifies exceptional objects according to a novel metric based on the entropy of membership values. We provide experimental results to demonstrate the effectiveness of our technique.

scholarly journals PENGELOMPOKAN KEJADIAN GEMPA BUMI MENGGUNAKAN FUZZY C-MEANS CLUSTERING

2019 ◽  
Vol 4 (2) ◽  
pp. 103-110
Author(s):  
Ryan Rifqi Arista ◽  
Rosa Andrie Asmara ◽  
Dwi Puspitasari
Keyword(s):  
Eurasian Plate ◽  
Tectonic Plates ◽  
Fuzzy C Means ◽  
Australian Plate ◽  
The Pacific ◽  
Earthquake Vulnerability ◽  
Fuzzy C Means Clustering ◽  
Grouping Systems ◽  
Two Parameters ◽  
High Level

The Indonesian region has a high level of earthquake vulnerability when compared to other countries. This is because Indonesia's position is at the confluence of three large tectonic plates namely the Eurasian plate, the Indo-Australian plate, and the Pacific plate. The high level of earthquake susceptibility is evidenced by significant earthquake data from 2005 to 2009, which recorded 26 significant earthquakes over a period of 4.8 to 8.6 on the Richter scale. The earthquake also caused impacts including casualties, injuries, damage to houses and destruction of houses.The earthquake event grouping system is a system that functions to classify earthquake events based on two main parameters, namely earthquake strength parameters and earthquake impact parameters. The two parameters are grouped separately, so that the grouping process produces two kinds of grouping results. The stages of this system start from preprocessing data to eliminate noise, then take grouping parameters from the user in the form of the number of clusters, minimum error values, and the maximum iteration limit. Grouping is done using fuzzy c-means method. The grouping results are then displayed in table form and in the form of coordinates in Google Maps.The grouping of earthquake events has been tested by comparing the results of grouping systems with the results of manual grouping. Testing is done by inputting a number of different maximum iterations. Based on the test results it was found that the greater the maximum iteration value will affect the accuracy of grouping.

scholarly journals Nurses’ knowledge of heart failure self-management education principles

2021 ◽  
Vol 9 (8) ◽  
pp. 2506
Author(s):  
Mohammad A. Abu Sa'aleek ◽  
Bader T. Al zawahra
Keyword(s):  
Heart Failure ◽  
Self Management ◽  
Level Of Education ◽  
The Usa ◽  
Level Of Knowledge ◽  
Care Practices ◽  
High Level ◽  
Educating Nurses ◽  
Nurses Knowledge

Heart failure is considered as a chronic disease and the management of such condition is complex and challenging. Nurses play a significant role in managing heart failure by enhancing self-care practices among patients. This paper aims to evaluate evidence from the literature regarding nurses level of knowledge about the educational principles in heart failure. The nine selected studies included a total number of 1181 patients. These studies were conducted in the USA and Europe from 2002 until 2019.the uniqueness of those selected studies that all the authors use the same instrument titled “nurses knowledge of heart failure education principles”. The results revealed that there was an inconsistency in the level of knowledge among nurses in hospital-based, ambulatory, primary care or home care settings. More randomized studies are needed to solve this discrepancy. The level of knowledge ranged from (60.4-79.85%). Six topics have been identified as areas of weakness in which education is needed. Educating nurses in different settings is the gold stander to raise their level of knowledge which in turn will be in a better position to provide a high level of education for patients in order to alleviate their suffering, improve the quality of life and reduce the frequent hospitalization.

scholarly journals Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering

2017 ◽  
Vol 63 (No. 8) ◽  
pp. 370-380 ◽  
Author(s):  
Jafarzadeh Ali Akbar ◽  
Mahdavi Ali ◽  
Jafarzadeh Heydar
Keyword(s):  
Forest Fire ◽  
Fire Risk ◽  
Apriori Algorithm ◽  
Factors Affecting ◽  
Wildfire Occurrence ◽  
Fuzzy C Means ◽  
Standing Dead ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Forest Fire Risk

In this study we evaluated forest fire risk in the west of Iran using the Apriori algorithm and fuzzy c-means (FCM) clustering. We used twelve different input parameters to model fire risk in Ilam Province. Our results with minimum support and minimum confidence show strong relationships between wildfire occurrence and eight variables (distance from settlement, population density, distance from road, slope, standing dead oak trees, temperature, land cover and distance from farm land). In this study, we defined three clusters for each variable: low, middle and high. The data regarding the factors affecting forest fire risk were distributed in these three clusters with different degrees of membership and the final map of all factors was classified by FCM clustering. Each layer was then created in a geographic information system. Finally, wildfire risks in the area obtained from overlaying these layers were classified into five categories, from very low to very high according to the degree of danger.

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