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The purpose of this study is to apply hybrid cluster analysis in classifying PT Pelindo I customers based on the level of customer satisfaction with passenger services of PT Pelindo I.

Hybrid cluster analysis is a combination of hierarchical and non-hierarchical cluster analysis. This hybrid cluster analysis appears to optimize the advantages of hierarchical and non-hierarchical methods simultaneously to obtain optimal grouping. Hybrid cluster analysis itself has high flexibility because it can combine all hierarchical and non-hierarchical methods without any limits in the order of analysis used.

The results showed that 72% of PT Pelindo I customers felt PT Pelindo I service was special, while the remaining 28% felt PT Pelindo I service was good.

In total, 117 customers of PT Pelindo I were involved in a study using the non-probability sampling method.

Examines the processes of cluster analysis and describes them using an example of benefit segmentation, and also discusses other applications suggesting new directions of research in related fields. Bases an example study with 200 early respondents to a survey into sixth formers' choice of degree course, in which students were given 23 criteria which related to their course choice. Comparisons of likeness using Euclidean distance measures were employed. Uses also importance ratings given by three drivers to characteristics of new cars. Proposes that hierarchical clustering can be criticised when used to cluster data that is not naturally hierarchical, but other procedures have similar failings. Posits that clumping and optimisation in conjunction with hierarchical clustering offer the greater potential. Concludes that cluster analysis is a flexible tool, which provides a number of opportunities for marketing, and it is an appealing and simple idea ‐ but there are many technical questions that a researcher must ask before it is used.

The purpose of this paper is to illustrate how qualitative data may be analysed using a method that can be considered as rigorous/scientific as any statistical analysis of quantitative data.

An artificial neural network programme CATPAC II™ was used to evaluate selected portions of two accounting standards: the Financial Reporting Standards Board of New Zealand's standard on consolidation; and the equivalent standard developed by the International Accounting Standards Committee and revised by the International Accounting Standards Board.

The analysis of the concepts of control in the two standards identifies the differences that exist between the two standards. These differences are illuminated through the use of a hierarchical cluster analysis of 40 unique concepts in each of the two standards and 2D representation of the concepts. The extent of the differences in the concepts was established through a rotational analysis of the two datasets.

This research is limited to the analysis of the concept of control and associated commentary paragraphs and supporting documents associated with two accounting standards. Different results may have been obtained had the whole standard been analysed.

Artificial neural network software can be used to support the intuitive textual understanding of the differences that exist in qualitative data. In this paper, the differences identified in the concepts of control may result in different interpretations being taken by the accounting standard users when determining what reporting entities to include in consolidated financial statements. Some additional uses for artificial neural network software in accounting research are also identified.

This paper is the first in the discipline to use artificial neural network software to analyse and compare different texts.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

The purpose of this paper is to evaluate the classification ability of pork quality by cluster analysis in relation to reference criteria proposed in the literature. Verify if clusters were theoretically significant with major pork quality categories. Verify if classificatory parameter values of quality attributes determined “a posteriori” may be used for following categorization.

In total, 60 pork loins were classified into pale, soft and exudative, reddish-pink, soft and exudative, RFN and dark, firm and dry by reference criteria and hierarchical cluster analyses were performed to identify groups of samples with different attributes, based on only pH45min and on pHu, L* and drip loss.

Cluster analysis divided total samples into different (p<0.05) smaller groups. Two groups were formed based on only pH45min and five groups were formed based on pHu, L* and drip loss. By these five groups, L* of 44 and 52 distinguished between dark, reddish-pink and pale meat colors and drip loss of 2 and 6 percent distinguished between dry, non-exudative and exudative meats. Cluster analyses identify pork groups with different attributes and the proposed parameters can be used to distinguish between groups theoretically similar to major pork quality categories.

To decide the best destination to pork carcass and to reduce economic losses, the correctly classify of the pork quality is decisive. This study proves that cluster analysis is able to classify pork into groups with significantly different quality attributes, which are significant with major pork quality categories, without unclassified samples.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

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.

This paper considers the classifications produced by application of the single linkage, complete linkage, group average and Ward clustering methods to the Keen and Cranfield document test collections. Experiments were carried out to study the structure of the hierarchies produced by the different methods, the extent to which the methods distort the input similarity matrices during the generation of a classification, and the retrieval effectiveness obtainable in cluster based retrieval. The results would suggest that the single linkage method, which has been used extensively in previous work on document clustering, is not the most effective procedure of those tested, although it should be emphasized that the experiments have used only small document test collections.

The purpose of the paper is to segment and profile the Indian shoppers in the context of organic foods in India. It proposes to use a healthy lifestyle (HL) as a segmenting variable and to use a factor-cluster analysis approach to achieve the same. The current study is expected to add a substantial base to the segmentation literature in marketing.

Food stores in Indian metropolitan city Chennai are sampled, and data is collected in the form of a mall intercept survey method. In total, 441 usable structured questionnaires are filled by the respondents which are subjected to suitable statistical analysis.

Three significantly different consumer segments emerged from the given sample of respondents, which shows uniqueness concerning consumer’s, HL features, demographics and the variables of the theory of planned behavior (TPB).

Clustering method used to segment the potential shoppers of organic foods is an exploratory technique only. It cannot be treated or generalized to the population like those of inferential techniques. The researcher suggested testing the same with a larger sample size and in a different context. It is limited to urban and suburban facets of the metropolitan city in India.

The study will be helpful to marketers and decision makers to target the potential organic foods consumers.

Community asset mapping (CAM) is an evidence-based activity commonly used in local socioeconomic development initiatives. Residents and other stakeholders collaboratively identify the resources that they deem most important to the vitality of their community. Results are depicted qualitatively using maps. While maps are a useful means to convey information, alternate approaches to summarize data drawn from CAM activities may yield additional inferences that better inform community development initiatives.

This study conducted a retrospective analysis of secondary, de-identified data collected from the 2015–2016 Gonzaga University Logan Neighborhood Asset Mapping Project. Hierarchical and nonhierarchical cluster analyses were used to establish interrelationships between the perceived importance of various community assets.

The hierarchical cluster analysis revealed a very intuitive hierarchical clustering of community assets, with various health care services tightly clustered together. Similarly, farmers’ markets, community gardens and meeting spaces were clustered closely together. Third, community education and care services for all age groups were clustered together. The nonhierarchical cluster analysis revealed intuitive clustering of respondent groups who valued particular sets of assets.

By identifying these clusters and characterizing the linkages between them, it is possible to fund multiple development initiatives that are mutually reinforcing. For example, if the neighborhood obtains funds to invest, then they could be used to facilitate both community gardens and farmers’ markets, two closely related activities. Additional physical locations might also be developed to support (possibly outdoor) meeting space.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2020-0206.