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The purpose of this paper is to present two novel grey cluster evaluation models to solve the difficulty in extending the bounds of each clustering index of grey cluster evaluation models.

In this paper, the triangular whitenization weight function corresponding to class 1 is changed to a whitenization weight function of its lower measures, and the triangular whitenization weight function corresponding to class s is changed to a whitenization weight function of its upper measures. The difficulty in extending the bound of each clustering indicator is solved with this improvement.

The findings of this paper are the novel grey cluster evaluation models based on mixed centre-point triangular whitenization weight functions and the novel grey cluster evaluation models based on mixed end-point triangular whitenization weight functions.

A practical evaluation and decision problem for some projects in a university has been studied using the new triangular whitenization weight function.

Particularly, compared with grey variable weight clustering model and grey fixed weight clustering model, the grey cluster evaluation model using whitenization weight function is more suitable to be used to solve the problem of poor information clustering evaluation. The grey cluster evaluation model using endpoint triangular whitenization weight functions is suitable for the situation that all grey boundary is clear, but the most likely points belonging to each grey class are unknown; the grey cluster evaluation model using centre-point triangular whitenization weight functions is suitable for those problems where it is easier to judge the most likely points belonging to each grey class, but the grey boundary is not clear.

The possibility function-based grey clustering model has evolved into a complete approach for dealing with uncertainty evaluation problems. Existing models still have problems with the choice dilemma of the maximum criteria and instances when the possibility function may not accurately capture the data's randomness. This study aims to propose a multi-stage skewed grey cloud clustering model that blends grey and randomness to overcome these problems.

First, the skewed grey cloud possibility (SGCP) function is defined, and its digital characteristics demonstrate that a normal cloud is a particular instance of a skewed cloud. Second, the border of the decision paradox of the maximum criterion is established. Third, using the skewed grey cloud kernel weight (SGCKW) transformation as a tool, the multi-stage skewed grey cloud clustering coefficient (SGCCC) vector is calculated and research items are clustered according to this multi-stage SGCCC vector with overall features. Finally, the multi-stage skewed grey cloud clustering model's solution steps are then provided.

The results of applying the model to the assessment of college students' capacity for innovation and entrepreneurship revealed that, in comparison to the traditional grey clustering model and the two-stage grey cloud clustering evaluation model, the proposed model's clustering results have higher identification and stability, which partially resolves the decision paradox of the maximum criterion.

Compared with current models, the proposed model in this study can dynamically depict the clustering process through multi-stage clustering, ensuring the stability and integrity of the clustering results and advancing grey system theory.

The purpose of this paper is to map the cluster in the leather and shoes sector for improving the competitiveness of the firms. Toward this end, the study is organized to examine the impact of clustering on competitiveness improvement. The influence of competitive elements and performance (Porter’s diamond) and balanced score card was utilized.

A random sample of 131 respondents was chosen during the period from May 2016 to July 2016. A structural equation modeling (SEM) analysis was applied to investigate the research model. This approach was chosen because of its ability to test casual relationships between constructs with multiple measurement items. Researchers proposed a two-stage model-building process for applying SEM. The measurement model was first examined for instrument validation, followed by an analysis of the structural model for testing associations hypothesized by the research model.

The main findings show that there is a unidirectional causal relationship between improvements of performance and achieve competitiveness and also reveal that the Palestinian shoes and leather cluster sector is vital and strong, and conclude that clustering can achieve competitiveness for small- and medium-sized enterprises.

Future research can examine the relationship between clustering and innovation. The effect of clustering using other clustering models other than Porter’s model is advised to be used for future research.

The relationships among clustering and competitiveness may provide a practical clue to both, policymakers and researchers on how cluster enhances economic firms such as a skilled workforce, research, development capacity, and infrastructure. This is likely to create assets such as trust, synergy, collaboration and cooperation for improved competitiveness.

The findings of this study provide background information that can simultaneously be used to analyze relationships among factors of innovation, customer’s satisfaction, internal business and financial performance. This study also identified several essential factors in successful firms, and discussed the implications of these factors for developing organizational strategies to encourage and foster competitiveness.

The purpose of this paper is to establish a two-stage grey cloud clustering model to assess the drought risk level of 18 prefecture-level cities in Henan Province.

The clustering process is divided into two stages. In the first stage, grey cloud clustering coefficient vectors are obtained by grey cloud clustering. In the second stage, with the help of the weight kernel clustering function, the general representation of the weight vector group of kernel clustering is given. And a new coefficient vector of kernel clustering that integrates the support factors of the adjacent components was obtained in this stage. The entropy resolution coefficient of grey cloud clustering coefficient vector is set as the demarcation line of the two stages, and a two-stage grey cloud clustering model, which combines grey and randomness, is proposed.

This paper demonstrates that 18 cities in Henan Province are divided into five categories, which are in accordance with five drought hazard levels. And the rationality and validity of this model is illustrated by comparing with other methods.

This paper provides a practical and effective new method for drought risk assessment and, then, provides theoretical support for the government and production departments to master drought information and formulate disaster prevention and mitigation measures.

The model in this paper not only solves the problem that the result and the rule of individual subjective judgment are always inconsistent owing to not fully considering the randomness of the possibility function, but also solves the problem that it’s difficult to ascertain the attribution of decision objects, when several components of grey clustering coefficient vector tend to be balanced. It provides a new idea for the development of the grey clustering model. The rationality and validity of the model are illustrated by taking 18 cities in Henan Province as examples.

The purpose of this study for eHealth text mining domains, cosine-based visual methods (VM) assess the clusters more accurately than Euclidean; which are recommended for tweet data models for clusters assessment. Such VM determines the clusters concerning a single viewpoint or none, which are less informative. Multi-viewpoints (MVP) were used for addressing the more informative clusters assessment of health-care tweet documents and to demonstrate visual analysis of cluster tendency.

In this paper, the authors proposed MVP-based VM by using traditional topic models with visual techniques to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets. The authors demonstrated the effectiveness of proposed methods on different real-time Twitter health-care data sets in the experimental study. The authors also did a comparative analysis of proposed models with existing visual assessment tendency (VAT) and cVAT models by using cluster validity indices and computational complexities; the examples suggest that MVP VM were more informative.

In this paper, the authors proposed MVP-based VM by using traditional topic models with visual techniques to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets.

In this paper, the authors proposed multi-viewpoints distance metric in topic model cluster tendency for the first time and visual representation using VAT images using hybrid topic models to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets.

The paper aims to develop (1) a comprehensive framework for classifying crowdshipping business models and (2) a taxonomy of currently implemented crowdshipping business models.

The business models of 105 companies offering crowdsourced delivery services are analysed. Cluster analysis and principal component analysis are applied to develop a business model taxonomy.

A detailed crowdsourced delivery business model framework with 74 features is developed. Based on it, six distinct clusters of crowdshipping business models are identified. One cluster stands out as the most appealing to customers based on social media metrics, indicating which type of crowdshipping business models is the most successful.

Detailed investigations of each of the six clusters and of recent crowdshipping business model developments are needed in further research in order to enhance the derived taxonomy.

This paper serves as a best-practices guide for both start-ups and global logistics operators for establishing or further developing their crowdsourced delivery business models.

This paper provides a holistic understanding of the business models applied in the crowdshipping industry and is a valuable contribution to the yet small amount of studies in the crowd logistics field.

The purpose of this paper is to explore and describe research presented in the International Journal of Quality & Reliability Management (IJQRM), thereby creating an increased understanding of how the areas of research have evolved through the years. An additional purpose is to show how text mining methodology can be used as a tool for exploration and description of research publications.

The study applies text mining methodologies to explore and describe the digital library of IJQRM from 1984 up to 2014. To structure and condense the data, k-means clustering and probabilistic topic modeling with latent Dirichlet allocation is applied. The data set consists of research paper abstracts.

The results support the suggestion of the occurrence of trends, fads and fashion in research publications. Research on quality function deployment (QFD) and reliability management are noted to be on the downturn whereas research on Six Sigma with a focus on lean, innovation, performance and improvement on the rise. Furthermore, the study confirms IJQRM as a scientific journal with quality and reliability management as primary areas of coverage, accompanied by specific topics such as total quality management, service quality, process management, ISO, QFD and Six Sigma. The study also gives an insight into how text mining can be used as a way to efficiently explore and describe large quantities of research paper abstracts.

The study focuses on abstracts of research papers, thus topics and categories that could be identified via other journal publications, such as book reviews; general reviews; secondary articles; editorials; guest editorials; awards for excellence (notifications); introductions or summaries from conferences; notes from the publisher; and articles without an abstract, are excluded.

There do not seem to be any prior text mining studies that apply cluster modeling and probabilistic topic modeling to research article abstracts in the IJQRM. This study therefore offers a unique perspective on the journal’s content.

This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions.

This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used.

This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust.

This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before modeling.

The purpose of this paper is to present a modeling approach for aggregate and disaggregate level models for cluster‐based diffusion of a new technology. The aggregate approach refers to the diffusion modeling of a product at the overall population level, while the disaggregate approach refers to the diffusion process at the individual entity level.

The pattern of diffusion of a new technology in a representative two‐cluster situation is studied. In the aggregate level modeling, a diffusion model is developed in which potential adopters of both clusters learn about the new technology from each other. This is done by a Lotka‐Volterra type of dynamical system of equations. Then, to focus on relatively micro‐level phenomena, such as different propensities of imitation and innovation of firms within a cluster, an agent‐based disaggregate model for cluster‐based diffusion of technology is proposed. In these disaggregate models, the effects of heterogeneity and the inter‐cluster and intra‐cluster distances between the agents are captured.

The results highlight two major points: first, both aggregate and disaggregate models are in agreement with each other, and second, both of the models exhibit a form similar to the Bass model. Thus, consistent with the general theme of why the Bass model fits without decision variables, it is found that the Bass model, when extended appropriately, can be expected to work well also in the cluster‐based technology diffusion situation.

This modeling approach can be applied to the modeling of those situations in which heterogeneous industrial units are present in geographical clusters. It can also be applied in the related contexts such as diffusion of practices (e.g. quality certifications) within a multi‐divisional organization or across various networked clusters.

For a homogenous population, the Bass model has been used extensively to predict the sales of newly introduced consumer durables. In comparison, little attention has been given to the modeling of the technology adoption by the industrial units present in disparate groups, called clusters. The major contribution of this paper is to propose a framework for cluster‐based diffusion of technological products, and then to present an analysis of that framework using two different methodologies.

For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round.

This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information.

While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly.

By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources.