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This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the Johor River basin during the northeast monsoon season. Time-based clustering is represented by employing dynamic time warping (DTW) dissimilarity measure, whereas non-time-based clustering is represented by employing Euclidean dissimilarity measure in analysing the Johor River discharge data. In addition, we combine each of these clustering methods with a frequency domain representation of the discharge data using Discrete Fourier Transform (DFT) to see if such transformation affects the clustering results. The clustering quality from the hierarchical data structures of the identified river discharge patterns for each of the methods is measured by the Cophenetic Correlation Coefficient (CPCC). The results from the time-based clustering using DTW based on DFT transformation show a higher CPCC value as compared to that of non-time-based clustering methods.

The purpose of this paper is to find the reason which the results of grey variable weight clustering method do not correspond with the reality. It proposes reconstructing the whitenization weight function, outlining why and how inconsistency is avoided. The study aims to improve the model of grey clustering method based on the whitenization weight function and list the steps of the new clustering model so that analysis and application of innovation capacity in a broader range is normally found.

First the reason for the problem that the clustering results of grey variable weight clustering do not correspond with the reality is analyzed in two existing literature. And then a new whitenization weight function is reconstructed, two properties of the whitenization weight function are proved. The solution of the new grey variable weight clustering based on the whitenization weight function is built by following six steps.

The paper provides a new whitenization weight function which satisfies the normative and non-triplecrossing. It suggests that successful clustering results of innovation capacity act on two levels: integrating the elements of innovation capacity indexes, and following steps of grey variable weight clustering.

This paper improves the existing method of grey variable weight clustering and fulfills an identified need to study how cities’ innovation capacity can be clustered.

The chapter aims to investigate the impact of the COVID-19 pandemic and crisis on the implementation of the game market strategy of clustering business structures. The chapter contributes to the literature by clarifying the concept of economic clustering from the perspectives of game theory and stakeholder theory in the COVID-19 pandemic and crisis. The scientific novelty and originality of the research results are that they revealed differences in the effectiveness of the game strategy of clustering business structures, first, between developed and developing countries and, second, between conditions of stability and conditions of crisis. The theoretical significance of the results and conclusions is that they opened a new perspective on the clustering of business structures – from the perspective of game theory (as a game strategy in its alternativity with the strategy of individual business presence in the market) and from the perspective of stakeholder theory (as a market strategy, the effectiveness of which is evaluated for all stakeholders). The practical significance of the research lies in the fact that it allows rationalising the decision-making on the implementation of the game strategy of clustering business structures in the context of the COVID-19 pandemic and crisis, considering the peculiarities of developed and developing countries. The authors provide their recommendations for each category of country.

This paper aims to determine the type of supplier involvement in terms of time and extent of supplier involvement in automobile product development based on the characteristics of parts in the Iranian automotive industry.

The paper proposes the clustering and analytic hierarchy process (AHP) methods. Combining the K-means clustering method and metaheuristic algorithms, the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are applied to achieve better clustering results.

The results show that lack of internal knowledge, high technology change and complexity of parts increase the need to outsource the design process. In addition to these reasons, high development costs and high interface complexity justify suppliers’ early involvement.

Most research only presents a conceptual framework for understanding the various levels of supplier involvement in new product development (NPD). However, in the automotive industry, numerous parts have differing degrees of importance and priority, and experts may have varying opinions based on different criteria. Therefore, the existing conceptual model for analyzing the types of involvement of each supplier is not practical. We have formulated a problem-solving approach that utilizes the clustering and AHP methods to analyze data obtained from qualitative research and determine the type of supplier involvement.

It is well known that the selection of initial cluster centers can significantly affect K-means clustering results. The purpose of this paper is to propose an improved, efficient methodology to handle such a challenge.

According to the fact that the inner-class distance among samples within the same cluster is supposed to be smaller than the inter-class distance among clusters, the algorithm will dynamically adjust initial cluster centers that are randomly selected. Consequently, such adjusted initial cluster centers will be highly representative in the sense that they are distributed among as many samples as possible. As a result, local optima that are common in K-means clustering can then be effectively reduced. In addition, the algorithm is able to obtain all initial cluster centers simultaneously (instead of one center at a time) during the dynamic adjustment.

Experimental results demonstrate that the proposed algorithm greatly improves the accuracy of traditional K-means clustering results and, in a more efficient manner.

The authors presented in this paper an efficient algorithm, which is able to dynamically adjust initial cluster centers that are randomly selected. The adjusted centers are highly representative, i.e. they are distributed among as many samples as possible. As a result, local optima that are common in K-means clustering can be effectively reduced so that the authors can achieve an improved clustering accuracy. In addition, the algorithm is a cost-efficient one and the enhanced clustering accuracy can be obtained in a more efficient manner compared with traditional K-means algorithm.

The grouping of parts and machines for design of cellular manufacturing systems is carried out by clustering analysis. Two major drawbacks of some clustering algorithms have been identified in handling bottleneck machines for forming machine cells. These drawbacks include solution inconsistency and possible misclustering which result in unnecessary bottleneck machines required. Presents a more robust clustering algorithm to overcome these drawbacks. The algorithm consists of four stages: selection of initial cluster centres; cluster‐seeking analysis; eliminating unnecessary bottleneck machines; and new parts assignments. The decision functions based on the formed machine cells are defined to assign new parts to the machine cells. The algorithm is capable of selecting an ideal set of initial cluster centres, and minimizing the number of bottleneck machines required for forming the desired number of machine cells. It can also provide alternative design of machine cells to accommodate the existing production environment.

The aim of this study is to provide an empirical investigation of the agricultural cluster’s economic, social and environmental values. By doing so, the authors aim to offer policymakers and decision-makers a strategic approach that promotes competitiveness and economic development through shared value.

The authors used AMOS software and applied structural equation modeling to achieve the study’s objectives. The study used this approach with path analysis through the Analysis of Moment Structures software.

The empirical results indicate that creating shared value (CSV) can enhance the agricultural sector’s competitiveness through clustering. Rather than enhancing competitiveness directly, CSV plays a crucial role in improving the relationship between clustering and competitiveness. The authors also examined Porter’s diamond of competitiveness and evaluated factors for creating a shared value strategy, such as factor conditions, demand conditions, related and supporting industries, strategy, structure, rivalry and the role of government.

This study focuses solely on the agricultural cluster in Qalqilya governorate and cannot be applied to other regions without additional research.

Ensuring that stakeholders in the agricultural sector are kept informed about the activities of the cluster and the benefits of their participation is crucial. Empirical findings and conclusions have demonstrated that a shared value strategy can enhance the competitiveness of this sector. To achieve this, institutions involved in developing the agricultural cluster must increase their efficiency and capacity. Consulting experts in this field and drawing on experiences from other countries can aid in achieving this goal. Additionally, enhancing farmers’ productivity should be a priority, and the Ministry of Agriculture can provide training and workshops to improve their skills and expertise.

This study suggests that Palestinian policymakers should establish effective partnerships between the government and the agricultural sector’s firms in Qalqilya to reinforce the cluster’s competitiveness. This strategy can stimulate competitiveness and promote economic and social development in Palestine.

This paper investigates share price clustering and its determinants across Nasdaq Stockholm, Copenhagen, Helsinki, and Iceland.

This paper investigates share price clustering and its determinants across Nasdaq Stockholm, Copenhagen, Helsinki, and Iceland. Univariate analysis confirms widespread clustering, notably favouring closing prices ending in zero. Multivariate analysis explores the impact of firm size, price level, volatility, and turnover on clustering.

Univariate analysis confirms widespread clustering, notably favouring closing prices ending in zero. Multivariate analysis explores the impact of firm size, price level, volatility, and turnover on clustering. Results reveal pervasive clustering, strengthening with higher prices and turnover but weakening with larger trade volumes, firm size, and smaller tick sizes. These empirical findings support the theoretical expectations of price negotiation and resolution hypotheses.

The observed clustering presents an opportunity for investors to potentially capitalize on this market anomaly and achieve supra-normal returns.

Price clustering, the phenomenon where certain price levels are traded more frequently, challenges the efficient market hypothesis and has been extensively studied in financial markets. However, the Scandinavian stock markets, particularly those in the Nasdaq Nordic Exchange, remain unexplored in this context.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.