Clustering algorithms are important for data mining, and K-means is one of the most well-known clustering algorithms currently available. In cases in which data are high-dimensional, however, mere...Show moreClustering algorithms are important for data mining, and K-means is one of the most well-known clustering algorithms currently available. In cases in which data are high-dimensional, however, mere application of K-means to a data set may fail to uncover clusters due to presence of masking variables, the curse of dimensionality, and difficulties in interpretation of the obtained solution. A commonly used work-around is to apply dimension reduction to the data prior to performing cluster analysis, a practice called Tandem Analysis (TA). A vulnerability of TA is that the applied dimension reduction is not guaranteed to preserve cluster structure present in the original data, jeopardising the usefulness of subsequent cluster analysis. Multiple authors have provided algorithms that reduce dimensionality of a data set and perform cluster analysis on the reduced data, either in a sequential fashion or a simultaneous fashion, all aiming to find suitable low-dimensional representations of data while also keeping cluster structures intact. In this thesis, a novel approach to reducing dimensionality and performing cluster analysis on the low dimensional representation of the data - called SICA - is described and thoroughly tested in two systematically manipulated simulation studies and applied to three empirical data applications. Results show that SICA is a computationally efficient algorithm well able to extract components from the original data that preserve cluster structures, but that performance depends on characteristics of the data and the model of data generation. In addition, the correctness and validity of the clusterings obtained through SICA is high, although it does not always outperform currently available methods in this regard and is dependent on the same characteristics of the data and model generation as the other algorithms. Limitations and implications for future research are discussed.Show less
Objectives. The current study aims to examine whether four burnout-engagement cluster groups could be identified based on burnout and engagement dimension scores, and to investigate whether these...Show moreObjectives. The current study aims to examine whether four burnout-engagement cluster groups could be identified based on burnout and engagement dimension scores, and to investigate whether these four cluster groups differed significantly in terms of job demands and job resources. Methods. A cross-sectional study, involving 877 professionals working at the Emergency Department, was carried out in 19 hospitals in the Netherlands in 2017. Burnout was assessed by the Utrecht Burnout Scale, work engagement with the 9-item Utrecht Work Engagement Scale, and job demands and job resources by the Leiden Quality of Work Questionnaire for nurses and doctors, and the Quality of Labor Questionnaire. Results. K-means cluster analysis revealed that four groups could be identified with varying levels of burnout complaints and engagement. However, CH-index showed that two main clusters, the burnout and engagement group, were best at describing the data. Subsequent MANOVA analysis revealed that the groups differed in terms of job demands and resources. Additionally, it was found that the engaged group experienced high resources and low demands compared to the burnout group with low resources and high demands. Conclusion. This study found evidence for a distinction between the burnout-engagement cluster groups regarding the burnout and engagement dimension scores. Furthermore, the findings provide confirmation that the burnout and engagement group significantly differed in terms of job demands and job resources. Future research should focus on various perceptions of Emergency Department professionals and longitudinal research.Show less