An Enhanced Fuzzy K-means Clustering with Application to Missing Data Imputation

In this paper an adjustment on the Fuzzy K-means (FKM) clustering method was suggested to improve the process of clustering. Also, a novel technique for missing data imputation was proposed and it was implemented twice: (1) using FKM and (2) using the Enhanced Fuzzy K-means (EFKM) clustering. The suggested model for imputing missing data consists of three phases: (1) Input Vectors Partitioning, (2) Enhanced Fuzzy Clustering, and(3) Missing Data Imputation. The implementation and experiments showed a clear improvement in the imputation accuracy in favor of the EFKM according to the value of RMSE

A Global-Relationship Dissimilarity Measure for the k

The k-modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed the k-modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships between the object and all cluster modes but also the differences of different attributes. Finally the experiments were made on four real data sets from UCI. And the corresponding results show that GRD achieves better performance than two existing dissimilarity measures used in k-modes and Cao’s algorithms

Robust K-Median and K-Means Clustering Algorithms for Incomplete Data

Incomplete data with missing feature values are prevalent in clustering problems. Traditional clustering methods first estimate the missing values by imputation and then apply the classical clustering algorithms for complete data, such as K-median and K-means. However, in practice, it is often hard to obtain accurate estimation of the missing values, which deteriorates the performance of clustering. To enhance the robustness of clustering algorithms, this paper represents the missing values by interval data and introduces the concept of robust cluster objective function. A minimax robust optimization (RO) formulation is presented to provide clustering results, which are insensitive to estimation errors. To solve the proposed RO problem, we propose robust K-median and K-means clustering algorithms with low time and space complexity. Comparisons and analysis of experimental results on both artificially generated and real-world incomplete data sets validate the robustness and effectiveness of the proposed algorithms

Robust K-Median and K-Means Clustering Algorithms for Incomplete Data

Incomplete data with missing feature values are prevalent in clustering problems. Traditional clustering methods first estimate the missing values by imputation and then apply the classical clustering algorithms for complete data, such as K-median and Kmeans. However, in practice, it is often hard to obtain accurate estimation of the missing values, which deteriorates the performance of clustering. To enhance the robustness of clustering algorithms, this paper represents the missing values by interval data and introduces the concept of robust cluster objective function. A minimax robust optimization (RO) formulation is presented to provide clustering results, which are insensitive to estimation errors. To solve the proposed RO problem, we propose robust K-median and K-means clustering algorithms with low time and space complexity. Comparisons and analysis of experimental results on both artificially generated and real-world incomplete data sets validate the robustness and effectiveness of the proposed algorithms

An Integrated Fuzzy Clustering Cooperative Game Data Envelopment Analysis Model with application in Hospital Efficiency

Hospitals are the main sub-section of health care systems and evaluation of hospitals is one of the most important issue for health policy makers. Data Envelopment Analysis (DEA) is a nonparametric method that has recently been used for measuring efficiency and productivity of Decision Making Units (DMUs) and commonly applied for comparison of hospitals. However, one of the important assumption in DEA is that DMUs must be homogenous. The crucial issue in hospital efficiency is that hospitals are providing different services and so may not be comparable. In this paper, we propose an integrated fuzzy clustering cooperative game DEA approach. In fact, due to the lack of homogeneity among DMUs, we first propose to use a fuzzy C-means technique to cluster the DMUs. Then we apply DEA combined with the game theory where each DMU is considered as a player, using Core and Shapley value approaches within each cluster. The procedure has successfully been applied for performances measurement of 288 hospitals in 31 provinces of Iran. Finally, since the classical DEA model is not capable to distinguish between efficient DMUs, efficient hospitals within each cluster, are ranked using combined DEA model and cooperative game approach. The results show that the Core and Shapley values are suitable for fully ranking of efficient hospitals in the healthcare systems

Organisational justice mechanisms' mediating leadership style, cognition- and affect-based trust during COVID-19 in South Africa

PURPOSE : This study aims to investigate how different kinds of leadership styles (transformational and transactional leadership) influence different components of trust (affect-based and cognition-based trust), mediated by organisational justice mechanisms (distributive, procedural and interactional justice) during COVID-19 conditions in South Africa. DESIGN/METHODOLOGY/APPROACH : This study conducted a quantitative study by collecting survey data from 366 leaders in three organisations in South Africa, using valid and reliable scales. Given the number of latent constructs, the statistical technique used for this research was partial least squares-structural equation modelling, which enabled the authors to evaluate the strength and significance of the mediating relationships. FINDINGS : Findings show unexpectedly that neither distributive nor procedural justice has any significant mediating effect between transformational and transactional leadership and between the components of trust (affect-based and cognition-based trust). However, interactional justice was found to have a significant positive mediating effect between transactional leadership and affect-based trust as well as cognition-based trust. The same did not apply to transformational leadership. ORIGINALITY/VALUE : Given the context of this study, which was conducted during the COVID-19 pandemic, these findings support the notion that it is the responsibility of leaders in organisations to communicate effectively, clearly and transparently to their followers at all times but particularly during times of extreme uncertainty. These increased levels of perceived fairness result in the development of trust within the organisation.https://www.emerald.com/insight/publication/issn/0955-534Xhj2023Gordon Institute of Business Science (GIBS