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This paper aims to effectively achieve endmembers and relative abundances simultaneously in hyperspectral image unmixing yield. Hyperspectral unmixing, which is an important step before image classification and recognition, is a challenging issue because of the limited resolution of image sensors and the complex diversity of nature. Unmixing can be performed using different methods, such as blind source separation and semi-supervised spectral unmixing. However, these methods have disadvantages such as inaccurate results or the need for the spectral library to be known a priori.

This paper proposes a novel method for hyperspectral unmixing called fuzzy c-means unmixing, which achieves endmembers and relative abundance through repeated iteration analysis at the same time.

Experimental results demonstrate that the proposed method can effectively implement hyperspectral unmixing with high accuracy.

The proposed method present an effective framework for the challenging field of hyperspectral image unmixing.

Data envelopment analysis (DEA) is a non-parametric model that is developed for evaluating the relative efficiency of a set of homogeneous decision-making units that each unit transforms multiple inputs into multiple outputs. However, usually the decision-making units are not completely similar. The purpose of this paper is to propose an algorithm for DEA applications when considered DMUs are non-homogeneous.

To reach this aim, an algorithm is designed to mitigate the impact of heterogeneity on efficiency evaluation. Using fuzzy C-means algorithm, a fuzzy clustering is obtained for DMUs based on their inputs and outputs. Then, the fuzzy C-means based DEA approach is used for finding the efficiency of DMUs in different clusters. Finally, the different efficiencies of each DMU are aggregated based on the membership values of DMUs in clusters.

Heterogeneity causes some positive impact on some DMUs while it has negative impact on other ones. The proposed method mitigates this undesirable impact and a different distribution of efficiency score is obtained that neglects this unintended impacts.

The proposed method can be applied in DEA applications with a large number of DMUs in different situations, where some of them enjoyed the good environmental conditions, while others suffered from bad conditions. Therefore, a better assessment of real performance can be obtained.

The paper proposed a hybrid algorithm combination of fuzzy C-means clustering method with classic DEA models for the first time.

The purpose of this paper is to propose hybrid revised weighted fuzzy c-means (RWFCM) clustering and Nelder–Mead (NM) simplex algorithm, called as RWFCM-NM, for generalized multisource Weber problem (MWP).

Although the RWFCM claims that there is no obligation to sequentially use different methods together, NM’s local search advantage is investigated and performance of the proposed hybrid algorithm for generalized MWP is tested on well-known research data sets.

Test results state the outstanding performance of new hybrid RWFCM and NM simplex algorithm in terms of cost minimization and CPU times.

Proposed approach achieves better results in continuous facility location problems.

It is essential to validate whether a nation's economic strength always transpires into new business capacity. The present research strives to identify the key indicators to the proxy new business ecosystem of countries and critically evaluate the similarity through the lens of advanced Fuzzy Clustering Frameworks over the years.

The authors use Fuzzy C Means, Type 2 Fuzzy C Means, Fuzzy Possibilistic C Means and Fuzzy Possibilistic Product Partition C Means Clustering algorithm to discover the inherent groupings of the considered countries in terms of intricate patterns of geospatial new business capacity during 2015–2018. Additionally, the authors propose a Particle Swarm Optimization driven Gradient Boosting Regression methodology to measure the influence of the underlying indicators for the overall surge in new business.

The Fuzzy Clustering frameworks suggest the existence of two clusters of nations across the years. Several developing countries have emerged to cater praiseworthy state of the new business ecosystem. The ease of running a business has appeared to be the most influential feature that governs the overall New Business Density.

It is of paramount practical importance to conduct a periodic review of nations' overall new business ecosystem to draw action plans to emphasize and augment the key enablers linked to new business growth. Countries found to lack new business capacity despite enjoying adequate economic strength can focus effectively on weaker dimensions.

The research proposes a robust systematic framework for new business capacity across different economies, indicating that economic strength does not necessarily transpire to equivalent new business capacity.

The purpose of this paper is to provide an analytical model for low carbon supplier development. This study is focused on the level of investment and collaboration decisions pertaining to emission reduction.

The authors’ model includes a fuzzy c-means (FCM) clustering algorithm and a fuzzy formal concept analysis. First, a set of suppliers were classified according to their carbon performances through the FCM clustering algorithm. Then, the fuzzy formal concepts were derived from a set of fuzzy formal contexts through an intersection-based method. These fuzzy formal concepts provide the relative level of investments and collaboration decisions for each identified supplier cluster. A case from the Indian renewable energy sector was used for illustration of the proposed analytical model.

The proposed model and case illustration may help manufacturing firms to collaborate with their suppliers for improving their carbon performances.

The study contributes to the low carbon supply chain management literature by identifying the decision criteria of investments toward low carbon supplier development. It also provides an analytical model of collaboration for low carbon supplier development. Though the purpose of the study is to illustrate the proposed analytical model, it would have been better if the model was empirically validated.

Though the earlier studies on green supplier development program evaluation have considered a set of criteria to decide whether or not to invest on suppliers, these are silent on the relative level of investment required for a given set of suppliers. This study aims to fulfill this gap by providing an analytical model that will help a manufacturing firm to invest and collaborate with its suppliers for improving their carbon performance.

Companies that wish to market a global brand need to develop a greater understanding of consumers' and potential consumers' susceptibility to global consumer culture (SGCC) with a view to standardizing/adapting their brand according to the desires and preferences of the consumers who belong to specific segments of global consumers. Thus, the aim of the study is to fill a joint segmentation research gap within and between countries based on seven dimensions of SGCC while classifying consumers according to the degree of belonging to specific and hybrid (global citizenship) segments.

A questionnaire was applied online in English in five countries across the Americas and Europe resulting in a sample of 412 consumers. Based on the fuzzy C-means cluster analysis, the study segments the sample of consumers according to the degree of belonging to specific and global citizenship segments.

Analysis of survey results show three groups; two distinct groups and a third with features of both, a distinct intersection group. These findings suggest that consumers in different countries develop beliefs and attitudes about global citizenship, and this perspective coincides with the characteristics of the intersection group. Consequently, the study shows that fragmentation of the needs of consumers exists within and between countries.

This study contributes to the concept of global citizenship, helping managers of global brands improve their marketing strategy decisions by implementing strategies that are standardized or adapted to specific hybrid segments of consumers that transcend national borders. This study used a statistical method to measure the degree of belonging to each segment.

Among the many accepted clustering techniques, the fuzzy clustering approaches have been developed over the last decades. These approaches have been applied to many areas in manufacturing systems. In this paper, a fuzzy clustering approach is proposed for selecting machine cells and part families in cellular manufacturing systems. This fuzzy approach offers a special advantage over existing clustering approaches as it presents the degree of membership of the machine or part associated with each machine cell or part family allowing users flexibility in formulating machine cells and part families. The proposed algorithm is extended and validated using numerical examples to demonstrate its application in cellular manufacturing.

The large volume of big data makes it impractical for traditional clustering algorithms which are usually designed for entire data set. The purpose of this paper is to focus on incremental clustering which divides data into series of data chunks and only a small amount of data need to be clustered at each time. Few researches on incremental clustering algorithm address the problem of optimizing cluster center initialization for each data chunk and selecting multiple passing points for each cluster.

Through optimizing initial cluster centers, quality of clustering results is improved for each data chunk and then quality of final clustering results is enhanced. Moreover, through selecting multiple passing points, more accurate information is passed down to improve the final clustering results. The method has been proposed to solve those two problems and is applied in the proposed algorithm based on streaming kernel fuzzy c-means (stKFCM) algorithm.

Experimental results show that the proposed algorithm demonstrates more accuracy and better performance than streaming kernel stKFCM algorithm.

This paper addresses the problem of improving the performance of increment clustering through optimizing cluster center initialization and selecting multiple passing points. The paper analyzed the performance of the proposed scheme and proved its effectiveness.

Because of the popularity of digital cameras, the number of personal photographs is increasing rapidly. In general, people manage their photos by date, subject, participants, etc. for future browsing and searching. However, it is difficult and/or takes time to retrieve desired photos from a large number of photographs based on the general personal photo management strategy. In this paper the authors aim to propose a systematic solution to effectively organising and browsing personal photos.

In their system the authors apply the concept of content‐based image retrieval (CBIR) to automatically extract visual image features of personal photos. Then three well‐known clustering techniques – k‐means, self‐organising maps and fuzzy c‐means – are used to group personal photos. Finally, the clustering results are evaluated by human subjects in terms of retrieval effectiveness and efficiency.

Experimental results based on the dataset of 1,000 personal photos show that the k‐means clustering method outperforms self‐organising maps and fuzzy c‐means. That is, 12 subjects out of 30 preferred the clustering results of k‐means. In particular, most subjects agreed that larger numbers of clusters (e.g. 15 to 20) enabled more effective browsing of personal photos. For the efficiency evaluation, the clustering results using k‐means allowed subjects to search for relevant images in the least amount of time.

CBIR is applied in many areas, but very few related works focus on personal photo browsing and retrieval. This paper examines the applicability of using CBIR and clustering techniques for browsing personal photos. In addition, the evaluation based on the effectiveness and efficiency strategies ensures the reliability of our findings.