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The implementation of hierarchic agglomerative methods of cluster anlaysis for large datasets is very demanding of computational resources when implemented on conventional computers. The ICL Distributed Array Processor (DAP) allows many of the scanning and matching operations required in clustering to be carried out in parallel. Experiments are described using the single linkage and Ward's hierarchical agglomerative clustering methods on both real and simulated datasets. Clustering runs on the DAP are compared with the most efficient algorithms currently available implemented on an IBM 3083 BX. The DAP is found to be 2.9–7.9 times as fast as the IBM, the exact degree of speed‐up depending on the size of the dataset, the clustering method, and the serial clustering algorithm that is used. An analysis of the cycle times of the two machines is presented which suggests that further, very substantial speed‐ups could be obtained from array processors of this type if they were to be based on more powerful processing elements.

The purpose of this paper is to develop a systematic approach for business customer segmentation.

This study proposes an approach for business customer segmentation that integrates clustering and multi-criteria decision making (MCDM). First, proper segmentation variables are identified and then customers are grouped by using hierarchical and partitional clustering algorithms. The approach extended the recency-frequency-monetary (RFM) model by proposing five novel segmentation variables for business markets. To confirm the viability of the proposed approach, a real-world application is presented. Three agglomerative hierarchical clustering algorithms namely “Ward’s method,” “single linkage” and “complete linkage,” and a partitional clustering algorithm, “k-means,” are used in segmentation. In the implementation, fuzzy analytic hierarchy process is employed to determine the importance of the segments.

Business customers of an international original equipment manufacturer (OEM) are segmented in the application. In this regard, 317 business customers of the OEM are segmented as “best,” “valuable,” “average,” “potential valuable” and “potential invaluable” according to the cluster ranks obtained in this study. The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation.

The success of the proposed approach relies on the availability and quality of customers’ data. Therefore, design of an extensive customer database management system is the foundation for any successful customer relationship management (CRM) solution offered by the proposed approach. Such a database management system may entail a noteworthy level of investment.

The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. By making customer segmentation decisions, the proposed approach can provides firms a basis for the development of effective loyalty programs and design of customized strategies for their customers.

The proposed segmentation approach may contribute firms to gaining sustainable competitive advantage in the market by increasing the effectiveness of CRM strategies.

This study proposes an integrated approach for business customer segmentation. The proposed approach differentiates itself from its counterparts by combining MCDM and clustering in business customer segmentation. In addition, it extends the traditional RFM model by including five novel segmentation variables for business markets.

In this study, supervision and control of the possible problems among people over a large area with a limited number of drone cameras and security staff is established.

These drones, namely unmanned aerial vehicles (UAVs) will be adaptively and automatically distributed over the crowds to control and track the communities by the proposed system. Since crowds are mobile, the design of the drone clusters will be simultaneously re-organized according to densities and distributions of people. An adaptive and dynamic distribution and routing mechanism of UAV fleets for crowds is implemented to control a specific given region. The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance.

The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance. An outperformed clustering performance from the aggregated model has been received when compared with a singular clustering method over five different test cases about crowds of human distributions. This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

This study aims to develop an artifact to measure the level of manufacturing competitiveness of a country in the global context and provide a suitable interpretation mechanism for the measured values, and to provide prescriptive solution where necessary so that the country can develop an actionable plan of program to move from the current level of global competitiveness to another such that they could provide more economic opportunities for their citizenry.

A manufacturing competitive index (MCI) was developed which includes relevant variables to capture a country’s manufacturing activity level in an economy with a balanced perspective. Reliable international sources were used. Ward algorithm was used to identify clear clusters of performance upon which competitive gaps were measured and improvement projects were identified and prioritized to obtain the best value for cluster transitional plan.

This study shows that the case country is not doing as well as it wants to believe, even when the relevant technology import measures were included in the expanded metric, but also, the next level of competitiveness is achievable within the national budget if proper prioritization is done.

The paper presents a cocktail of indexes that is more exhaustive of MCI, including both research capacity and technology import variables. It also uses clustering mechanism to provide a proper context to interpret the MCI scores in the context of peer nations. It presents a gap determination methodology and shows how priority projects could be logically selected to close measured gaps based on anticipated value from budget expenses

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.

The purpose of this paper is to propose a new methodology that handles the issue of the dynamic behavior of customers over time.

A new methodology is presented based on time series clustering to extract dominant behavioral patterns of customers over time. This methodology is implemented using bank customers’ transactions data which are in the form of time series data. The data include the recency (R), frequency (F) and monetary (M) attributes of businesses that are using the point-of-sale (POS) data of a bank. This data were obtained from the data analysis department of the bank.

After carrying out an empirical study on the acquired transaction data of 2,531 business customers that are using POS devices of the bank, the dominant trends of behavior are discovered using the proposed methodology. The obtained trends were analyzed from the marketing viewpoint. Based on the analysis of the monetary attribute, customers were divided into four main segments, including high-value growing customers, middle-value growing customers, prone to churn and churners. For each resulted group of customers with a distinctive trend, effective and practical marketing recommendations were devised to improve the bank relationship with that group. The prone-to-churn segment contains most of the customers; therefore, the bank should conduct interesting promotions to retain this segment.

The discovered trends of customer behavior and proposed marketing recommendations can be helpful for banks in devising segment-specific marketing strategies as they illustrate the dynamic behavior of customers over time. The obtained trends are visualized so that they can be easily interpreted and used by banks. This paper contributes to the literature on customer relationship management (CRM) as the proposed methodology can be effectively applied to different businesses to reveal trends in customer behavior.

In the current business condition, customer behavior is changing continually over time and customers are churning due to the reduced switching costs. Therefore, choosing an effective customer segmentation methodology which can consider the dynamic behaviors of customers is essential for every business. This paper proposes a new methodology to capture customer dynamic behavior using time series clustering on time-ordered data. This is an improvement over previous studies, in which static segmentation approaches have often been adopted. To the best of the authors’ knowledge, this is the first study that combines the recency, frequency, and monetary model and time series clustering to reveal trends in customer behavior.

This paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.

Because of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.

The results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.

The novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.

The proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.

Does a company’s country of incorporation or the sector of its activity have a greater influence on the equity returns its shareholders earn? This question has been examined extensively using dummy variable regressions or factor models on pre‐determined characteristics; nevertheless, the results are inconclusive and vary with the range of companies and the time period studied. This study employs an alternative method that finds “naturally occurring” groups of companies based on the quantifiable relationship between the company returns themselves. The resulting groups are then examined in terms of their country and sector composition. The groups indicate that companies clearly cluster by country rather than by sector and that this effect has become more pronounced over time. This has important implications for financial analysts and portfolio managers.

Strategic groups research has been hampered by the poor alignment between theory and methods. This has been due in large part to the lack of significance tests for cluster analysis. Now that significance tests are available, the theoretical and methodological implications are discussed. The paper aims to discuss these issues.

The theory behind strategic groups is reframed to capitalize on the available significance tests. Subsequently, the significance tests are also modified to fit the proposed theory. Due to this integrative approach, this is both a theoretical and a methodological paper.

In lieu of significance tests, finding differences in performance emerged as the litmus test for the existence of discrete strategic groups. The concept of strategic groups gradually evolved to fit this requirement. Now that significance tests are available, these legacy effects of the structure-performance link can be removed. This reveals that three conflicting concepts have been sharing the label of strategic groups: strategic categories, interdependent strategic groups and strategic performance groups. The theory also reveals that the significance tests developed in ecological research need modifications for use in strategic groups research.

A theory is proposed for interdependent strategic groups and a significance test of external isolation is proposed as part of this integrative solution.

This integrative solution appears to resolve the historical mismatch between theory and methods that has plagued this field since its inception. This creates a variety of intriguing areas for future research.

This study aims to examine and visualize the adopted internal control structure and effectiveness in firms and present a typology of firms. Control structure and effectiveness are measured based on the assessment of management, rather than using reported material weaknesses as most studies do. This type of evaluation is more purposeful for firms that do not apply the Sarbanes-Oxley Act. Internal control frameworks provide only broad guidance concerning internal control concepts, leaving the details to the adopting firms.

The survey data (from 741 CEOs) are clustered using the self-organizing map, a visual artificial neural network approach. A three-dimensional effectiveness proxy is used.

The analysis reveals four alternative types of internal control effectiveness in firms and visually presents how the components of the internal control structure are associated with each one. A typology of internal control structure and effectiveness is then created.

The findings suggest that there are interrelated, but not straightforward, relationships between internal control variables and that there is a link between some of them and higher internal control effectiveness in practice. These findings have important implications for those responsible for improving or assessing internal control, such as management, personnel and internal and external auditors.

This paper uses a clustering approach to create a typology for alternative types of internal control structure and effectiveness, based on data from actual firms. Instead of using material weaknesses as a measure, this study uses managers’ own assessments of internal control effectiveness.