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Constrained clustering is an important recent development in clustering literature. The goal of an algorithm in constrained clustering research is to improve the quality of clustering by making use of background knowledge. The purpose of this paper is to suggest a new perspective for constrained clustering, by finding an effective transformation of data into target space on the reference of background knowledge given in the form of pairwise must- and cannot-link constraints.

Most of existing methods in constrained clustering are limited to learn a distance metric or kernel matrix from the background knowledge while looking for transformation of data in target space. Unlike previous efforts, the author presents a non-linear method for constraint clustering, whose basic idea is to use different non-linear functions for each dimension in target space.

The outcome of the paper is a novel non-linear method for constrained clustering which uses different non-linear functions for each dimension in target space. The proposed method for a particular case is formulated and explained for quadratic functions. To reduce the number of optimization parameters, the proposed method is modified to relax the quadratic function and approximate it by a factorized version that is easier to solve. Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed method.

This study proposes a new direction to the problem of constrained clustering by learning a non-linear transformation of data into target space without using kernel functions. This work will assist researchers to start development of new methods based on the proposed framework which will potentially provide them with new research topics.

Class-based storage has been studied extensively and proved to be an efficient storage policy. However, few literature addressed how to cluster stuck items for class-based storage. The purpose of this paper is to develop a constrained clustering method integrated with principal component analysis (PCA) to meet the need of clustering stored items with the consideration of practical storage constraints.

In order to consider item characteristic and the associated storage restrictions, the must-link and cannot-link constraints were constructed to meet the storage requirement. The cube-per-order index (COI) which has been used for location assignment in class-based warehouse was analyzed by PCA. The proposed constrained clustering method utilizes the principal component loadings as item sub-group features to identify COI distribution of item sub-groups. The clustering results are then used for allocating storage by using the heuristic assignment model based on COI.

The clustering result showed that the proposed method was able to provide better compactness among item clusters. The simulated result also shows the new location assignment by the proposed method was able to improve the retrieval efficiency by 33 percent.

While number of items in warehouse is tremendously large, the human intervention on revealing storage constraints is going to be impossible. The developed method can be easily fit in to solve the problem no matter what the size of the data is.

The case study demonstrated an example of practical location assignment problem with constraints. This paper also sheds a light on developing a data clustering method which can be directly applied on solving the practical data analysis issues.

The aim of this study is to propose a deep neural network (DNN) method that uses side information to improve clustering results for big datasets; also, the authors show that applying this information improves the performance of clustering and also increase the speed of the network training convergence.

In data mining, semisupervised learning is an interesting approach because good performance can be achieved with a small subset of labeled data; one reason is that the data labeling is expensive, and semisupervised learning does not need all labels. One type of semisupervised learning is constrained clustering; this type of learning does not use class labels for clustering. Instead, it uses information of some pairs of instances (side information), and these instances maybe are in the same cluster (must-link [ML]) or in different clusters (cannot-link [CL]). Constrained clustering was studied extensively; however, little works have focused on constrained clustering for big datasets. In this paper, the authors have presented a constrained clustering for big datasets, and the method uses a DNN. The authors inject the constraints (ML and CL) to this DNN to promote the clustering performance and call it constrained deep embedded clustering (CDEC). In this manner, an autoencoder was implemented to elicit informative low dimensional features in the latent space and then retrain the encoder network using a proposed Kullback–Leibler divergence objective function, which captures the constraints in order to cluster the projected samples. The proposed CDEC has been compared with the adversarial autoencoder, constrained 1-spectral clustering and autoencoder + k-means was applied to the known MNIST, Reuters-10k and USPS datasets, and their performance were assessed in terms of clustering accuracy. Empirical results confirmed the statistical superiority of CDEC in terms of clustering accuracy to the counterparts.

First of all, this is the first DNN-constrained clustering that uses side information to improve the performance of clustering without using labels in big datasets with high dimension. Second, the author defined a formula to inject side information to the DNN. Third, the proposed method improves clustering performance and network convergence speed.

Little works have focused on constrained clustering for big datasets; also, the studies in DNNs for clustering, with specific loss function that simultaneously extract features and clustering the data, are rare. The method improves the performance of big data clustering without using labels, and it is important because the data labeling is expensive and time-consuming, especially for big datasets.

Tens of thousands of news articles are posted online each day, covering topics from politics to science to current events. To better cope with this overwhelming volume of information, RSS (news) feeds are used to categorize newly posted articles. Nonetheless, most RSS users must filter through many articles within the same or different RSS feeds to locate articles pertaining to their particular interests. Due to the large number of news articles in individual RSS feeds, there is a need for further organizing articles to aid users in locating non‐redundant, informative, and related articles of interest quickly. This paper aims to address these issues.

The paper presents a novel approach which uses the word‐correlation factors in a fuzzy set information retrieval model to: filter out redundant news articles from RSS feeds; shed less‐informative articles from the non‐redundant ones; and cluster the remaining informative articles according to the fuzzy equivalence classes on the news articles.

The clustering approach requires little overhead or computational costs, and experimental results have shown that it outperforms other existing, well‐known clustering approaches.

The clustering approach as proposed in this paper applies only to RSS news articles; however, it can be extended to other application domains.

The developed clustering tool is highly efficient and effective in filtering and classifying RSS news articles and does not employ any labor‐intensive user‐feedback strategy. Therefore, it can be implemented in real‐world RSS feeds to aid users in locating RSS news articles of interest.

The current paper describes various co‐branding methods that are available to franchisors and franchisees. The paper also presents an exploratory study that provides some insight into the activities in which franchisors in the food service industry may be willing to engage, in collaboration with other firms, when entering and maintaining co‐branding relationships. A sample of International Franchise Association (IFA) members was selected for the survey.

Presents the story of a managing director and his team adopting management development as a positive contributor to a massive and urgent change of strategy within a company. Examines the way that total cultural change within the company was achieved. Focuses particularly on those processes, structures and systems that are relevant to people development.

The Dictionary of Canadian Biography (called the DCB; see sidebar 1) is a companion to Britain's Dictionary of National Biography and America's Dictionary of American Biography. At first glance, the DCB in title, concept, and general appearance is a parallel publication—the national and monumental biographical dictionary which records, posthumously, the lives of persons who contributed to the nation. However, there are some differences. These reflect not so much the concept of a national biography as the economic and editorial circumstances surrounding such a project in Canada in the last half of the twentieth century.

This study aims to examine the impact of socioeconomic factors on electricity usage and assess the feasibility of implementing a mini-grid system in Kasangazi, Malawi. The primary aim is to understand the community’s current and potential utilisation of electrical equipment.

A mixed-methods approach was used to collect quantitative and qualitative data. Information was gathered through structured questionnaires, and energy audits were conducted among 87 randomly selected households from 28 Kasangazi communities. Data analysis relied on descriptive statistics using IBM SPSS version 28.

The study indicates that every household in Kasangazi uses non-renewable energy sources: 60 households use disposable batteries for lighting, 20 for radios and all use firewood, freely sourced from local forests, for cooking and heating water. The study shows that firewood is the community’s preferred energy source, illustrating the challenges faced in the fight against deforestation. Most household income comes from farming, with smaller contributions from businesses, employment and family remittances. Access to higher education is scarce, with only one out of 349 family members receiving tertiary education. Despite the constraints of low education levels and income, there is a demand for larger electrical appliances such as stoves and refrigerators. This underscores the need for mini-grid solutions, even in less technologically advanced, agriculture-dependent communities.

This study underscores that in Sub-Saharan Africa, factors like household size, income and education levels do not significantly influence the electricity demand but should be taken as part of the fundamental human rights. Rural populations express a desire for electricity due to the convenience it offers, particularly for appliances like refrigerators and stoves. Mini-grids emerge as a viable alternative in regions where grid electricity provision is challenging. It is concluded from this paper that the issue of using renewable energy should not only be taken for environmental preservation but also to promote energy access, augmenting efforts in supplying electricity to the remotest parts of the country.

Presently, existing electric car sharing platforms are based on a centralized architecture which are faced with inadequate trust and pricing issues as these platforms requires an intermediary to maintain users’ data and handle transactions between participants. Therefore, this article aims to develop a decentralized peer-to-peer electric car sharing prototype framework that offers trustable and cost transparency.

This study employs a systematic review and data were collected from the literature and existing technical report documents after which content analysis is carried out to identify current problems and state-of-the-art electric car sharing. A use case scenario was then presented to preliminarily validate and show how the developed prototype framework addresses the trust-lessness in electric car sharing via distributed ledger technologies (DLTs).

Findings from this study present a use case scenario that depicts how businesses can design and implement a distributed peer-to-peer electric car sharing platforms based on IOTA technology, smart contracts and IOTA eWallet. Main findings from this study unlock the tremendous potential of DLT to foster sustainable road transportation. By employing a token-based approach this study enables electric car sharing that promotes sustainable road transportation.

Practically the developed decentralized prototype framework provides improved cost transparency and fairness guarantees as it is not based on a centralized price management system. The DLT based decentralized prototype framework aids to orchestrate the incentivize monetization and rewarding mechanisms among participants that share their electric cars enabling them to collaborate towards lessening CO₂ emissions.

The findings advocate that electric vehicle sharing has become an essential component of sustainable road transportation by increasing electric car utilization and decreasing the number of vehicles on the road.

The key novelty of the article is introducing a decentralized prototype framework to be employed to develop an electric car sharing solution without a central control or governance, which improves cost transparency. As compared to prior centralized platforms, the prototype framework employs IOTA technology smart contracts and IOTA eWallet to improve mobility related services.