Cluster Analysis Method Research Articles

Description of The Phenotypic Characteristics of Some Tomato Genotypes

This study was carried out in order to determine some morphological characteristics of 240 tomato genotypes at S2 level and to demonstrate the usability of these materials in breeding programs. Accordingly, the average fruit weight was between 553.11 gr and 74.83 gr. The mean pericarp thickness, carpel number, and soluble dry matter content of the genotypes were 7.38 mm, 3.6 pieces and 4.04% brix, respectively. Based on these measurements and observations, tomato genotypes were also investigated using the Cluster and Principal Component Analysis (PCA) methods. According to the PCA results, the first component explained 24.1% of the total variance, and the number of fruit carpels, average fruit weight, and fruit width were the most highly explained parameters. The second and third main components constituted 13.06% and 11.16% of the total. Based on the results of fruit characterization studies, genotypes 233, 447, 126, 22, 391, 118, 71, 100, 340, 102 were determined as superior. Overall, A high degree of morphological variation was detected among the characterized tomato genotypes.

초등교사의 수업실행 저해요인에 관한 연구

Objectives This study focused on the factors which inhibit the teaching practices as they were perceived by elementary school teachers. Methods Thirty-five elementary school teachers were interviewed, and research materials were analyzed using a concept map approach. From the interview, 44 key sentences were extracted as a final statement, and the concept mapping was completed using the Multidimensional Scaling and Hierarchical Cluster Analysis method. 970 elementary school teachers were asked to answered the 5-point Likert scale questionnaires to check which statement could affect the teaching practices. Results First, the elementary school teachers recognized the factors inhabiting the teaching practices in the “job performance-relationship formation” and “inside-outside the school” dimensions. Second, elementary school teachers recognized the inhabiting factors in 8 clusters; “formalism of educational administration,” “inefficiency of school work,” “lack of trust between teachers and parents,” “lack of efficiency in business processing,” “lack of administrators’ leadership,” “lack of teaching support,” “negative teaching culture,” and “lack of professionalism and teacher’s philosophy” appeared in that order. Conclusions This study will help to identify ways to improve the teaching expertise by identifying the inhibiting factors as perceived by elementary school teachers.

Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data

Abstract. The properties of aerosols are highly uncertain owing to the complex changes in their composition in different regions. The radiative properties of different aerosol types differ considerably and are vital for studying aerosol regional and/or global climate effects. Traditional aerosol-type identification algorithms, generally based on cluster or empirical analysis methods, are often inaccurate and time-consuming. In response, our study aimed to develop a new aerosol-type classification model using an innovative hybrid algorithm to improve the precision and efficiency of aerosol-type identification. This novel algorithm incorporates an optical database, constructed using the Mie scattering model, and employs a random forest algorithm to classify different aerosol types based on the optical data from the database. The complex refractive index was used as a baseline to assess the performance of our hybrid algorithm against the traditional Gaussian kernel density clustering method for aerosol-type identification. The hybrid algorithm demonstrated impressive consistency rates of 90 %, 85 %, 84 %, 84 %, and 100 % for dust, mixed-coarse (mixed, course-mode aerosol), mixed-fine (mixed, fine-mode aerosol), urban/industrial, and biomass burning aerosols, respectively. Moreover, it achieved remarkable precision, with evaluation metric indexes for micro-precision, micro-recall, micro-F1-score, and accuracy of 95 %, 89 %, 91 %, and 89 %, respectively. Lastly, a global map of aerosol types was generated using the new hybrid algorithm to characterize aerosol types across the five continents. This study, utilizing a novel approach for the classification of aerosol, will help improve the accuracy of aerosol inversion and determine the sources of aerosol pollution.

Investigating the financialization of water infrastructure in China: A public policy perspective

ABSTRACT Insufficient funding for water infrastructure has become a notable challenge for China and other developing countries. In light of marketization and globalization, financialization has emerged as a potential avenue to address the financial difficulties. This study employs the PMC index model and cluster analysis method to quantitatively analyze the financialization of water infrastructure in China through a lens of public policy lens. The findings suggest that financialization has become a significant trend in water infrastructure, with public policy playing a critical role in water infrastructure development. This study contributes to the novel possibilities for studying water infrastructure financialization and offers a reference to policymakers and professionals for optimizing water infrastructure development.

Optimization research on automatic pricing and replenishment decision method for vegetable commodities

This paper addresses the issues of replenishment and pricing of vegetable commodities in fresh food supermarkets. By analyzing sales data and loss rate data, mathematical models are established to solve the following problems. Spearman correlation analysis and cluster analysis methods are employed. Firstly, data preprocessing is conducted by excluding returned vegetable products from a certain supermarkets distribution, and then integrating sales transaction details and wholesale price-related data. Secondly, monthly data is used to succinctly present the sales distribution patterns of various categories and individual items of vegetables through line charts. Finally, the models of Spearman correlation analysis and cluster analysis are respectively applied to illustrate the relationship between the sales volume of 6 major categories of vegetables and 251 individual items. Linear regression models, time series analysis, and XG-Boost regression analysis models are used. This paper requires replenishment plans to be made by category, analyzing the relationship between the total sales volume and cost-plus pricing of various vegetable categories. Firstly, a linear regression model is applied to fit historical sales volume and cost-plus pricing, resulting in a linear functional relationship, indicating that only the sales volume of tomatoes is negatively correlated with cost-plus pricing. Based on historical sales data, the daily replenishment total for each vegetable category within the next week is predicted. Predictive models and time series analysis are employed. Meanwhile, for pricing strategies, the XGBoost algorithm is utilized to provide reasonable pricing strategies. Based on the optimal solution, replenishment plans and pricing strategies for the next week are formulated to ensure the maximization of sales revenue objectives.

Dynamic evolution of ultrafiltration membrane fouling affected by S(IV)–Fe(II)-activated permanganate pretreatment during a long-term operation

ABSTRACT The S(IV)–Fe(II)/PM pretreatment has demonstrated preliminary potential as an effective ultrafiltration (UF) pretreatment technology. However, a comprehensive understanding of its impact on UF membrane fouling control and the dynamic evolution of membrane fouling during prolonged operation is still lacking. In this study, a relatively prolonged fouling experiment was conducted. Results revealed that the S(IV)–Fe(II)/PM pretreatment exhibited superior performance over Al(III) coagulation pretreatment in mitigating the transmembrane pressure difference and addressing both reversible and irreversible membrane fouling. The application of a cluster analysis method to classify membrane fouling evolution stages further confirmed that S(IV)–Fe(II)/PM pretreatment effectively decelerated the rate of membrane fouling evolution. The surface cake layer of UF membranes pretreated with S(IV)–Fe(II)/PM exhibited greater looseness and smoothness. It also showed better results than Al(III) coagulation pretreatment in reducing the accumulation of organic foulants, controlling the Si content and reducing the total microorganisms and live microorganisms in the UF feed water. Variance Partitioning Analysis indicated that the combined contribution of organic, inorganic, and biological foulants was the most significant for UF membranes after S(IV)–Fe(II)/PM pretreatment (50.4%) and UF membranes after Al(III) coagulation pretreatment (70.2%). These findings underscore the efficacy of S(IV)–Fe(II)/PM pretreatment in controlling UF membrane fouling under prolonged operation.

The integration algorithm of digital resources in business administration based on cluster analysis

The field of business management involves a large amount of data and information sources, including market data, customer data, supply chain data, etc. In order to quantify and analyze different resources, help enterprises better plan and allocate resources, and improve resource utilization efficiency, a clustering analysis based digital resource integration algorithm for business management is studied. Build a business management digital resource integration framework, including data layer, integration layer, and storage layer, to integrate and store data from different sources of business management databases, thereby facilitating unified management and utilization of digital resources by enterprises. The data layer collects data from different business management databases and stores it in the database according to different sources; The integration layer preprocesses the collected data, simply fixes errors and missing information in the data, and improves data quality. Adopting a feature extraction method based on the projection direction uncorrelation strategy of the labeled power set conversion method, the useful feature information of digital resources in enterprise management can be effectively extracted; Based on the two-step clustering analysis method, business management digital resources are clustered according to similar characteristics to complete the classification and integration of business management digital resources, and improve the efficiency of resource utilization; The storage layer adopts the Security Information Diffusion Algorithm (IDA) storage model to store integrated and classified digital resources managed by enterprises, ensuring data security and effectively preventing data leakage and illegal access. The experimental results show that the digital resource structure of business management integrated by this algorithm is clear, with a data redundancy of less than 8% and a difference of less than 11%. The time consumption for data integration is less than 2.11 minutes, indicating good resource integration ability.

Financial priorities and the growth border of the Russian economy

Subject. The article investigates the role of foreign exchange reserves in the development of the Russian economy, the securities market development, and the banking system stability. Objectives. The aim is to review the monetary policy options under international sanctions strengthening. Methods. I apply methods of statistical, neural network, and cluster analysis. Results. The study established that it is impossible to reduce currency risks by issuing shares listed in the domestic market in foreign currency. The preferred directions of Russia's monetary policy are as follows: strengthening the country's international investment position, increasing the volume of the Federal bond market, and increasing the issue of shares listed in the domestic market in rubles. Conclusions. The growth boundary of the Russian economy is determined by such parameters as the amount of funds in escrow accounts of individuals, the size of insurance and pension reserves.

ПУТИ ВОВЛЕЧЕНИЯ НЕВОСТРЕБОВАННЫХ СЕЛЬСКОХОЗЯЙСТВЕННЫХ ЗЕМЕЛЬ ВЛАДИМИРСКОЙ ОБЛАСТИ В ЭКОНОМИЧЕСКИЙ ОБОРОТ

The article, using the example of the territories of the Troitsky and Novomoskovsky administrative districts (hereinafter referred to as TiNAO) of Moscow, shows the possibility of a geoecological assessment of changes in the morphometric parameters of small reservoirs. Before joining the administrative structure of the Moscow territories in 2012, the studies on the ecology of these territories was minimal. Of particular importance for monitoring the geo-ecological situation in “New Moscow», – what the conjoined territories are also called, is the fact that its area is 1,480 km2, which is 57.8% of the entire territory of Moscow.
 The subject of the study were 1402 small reservoirs registered in the territory of the TiNAO. To carry out their environmental assessment, a cluster analysis method was used, which is a universal mathematical tool for scientific research. A negative change (decrease) in the average depth of the reservoir was taken as the main parameter of the study. Based on the results of the objects’ morphometry analysis carried out at an interval of two years, data on dynamic changes in the physical parameters of reservoirs were obtained and processed. It was established that shallowing problems exist in 4.51% of the total area of reservoirs in the TiNAO. Mapping stagnant small reservoirs made it possible to visually assess the scale of the problem and identify potentially problematic areas of the TiNAO from a geoecological point of view. Using the cluster analysis, zoning of problematic water bodies was carried out. Such identification (clustering) of water bodies’ grouping made it possible to see deviations at the level of groups of small river basins, which provided a large-scale understanding of the ecological situation at the level of catchment areas of larger water bodies.

Sectoral Performance Trends and Differences in the Balkan and Eastern European Region

This article provides an empirical analysis aimed at evaluating the financial trends and disparities at the sector level within the Balkan and Eastern European region. The dataset encompasses a period of nine years and comprises more than 20 million firm-year observations from 24 industries in 21 countries. It uses 19 financial ratios to assess sectoral performance. In the empirical investigation, trend analysis and the two-step cluster analysis methods were used. Following the global financial crisis, a significant proportion of financial ratios exhibited favorable trends, indicating robust business and economic circumstances. Nevertheless, this trajectory was temporarily disrupted in 2020 due to the onset of the COVID-19 pandemic. By 2021, the financial ratios had reverted back to their historical patterns. Country membership, margin, liquidity, trade turnover, profitability, and leverage ratios are the most effective variables for explaining differences in sectoral performance. Sector membership is a comparatively less influential factor. Although this study effectively identified significant disparities in financial ratio profiles, it does not suggest that companies in the most developed countries in the region attain the most favorable financial performance. Stakeholders who have a vested interest in this region should carefully contemplate the ramifications of the findings from this study.

Cluster analysis applied to obtaining reference models for building thermal performance studies

Cluster analysis has often been used to obtain reference models. However, using different methods in each analysis step may yield different results. Furthermore, the validation of the results is often neglected. This study aims to develop a cluster analysis method to obtain reference models for use in thermal performance studies of buildings from different clustering configurations. The procedure proposed is composed of six steps: (1) initial database composition, (2) data matrices formation, (3) cluster analysis, (4) determination of reference models, (5) methods validation and (6) results interpretation. Different data treatments, similarity measures and partitioning algorithms were combined in the cluster analysis to define the method with the best cluster formation. The weighting factor was the data treatment that most contributed to obtaining suitable clustering solutions. City-block, Euclidean Distance and Squared Euclidean Distance similarity measures resulted in suitable formations as well as Complete Linkage, Ward and K-means algorithms. Two clusters were obtained from the cluster methodology, for which two reference models were determined. Hypothesis tests showed that clusters differ for most performance indicators. It was concluded that the procedure proposed could identify the combination of methods that results in the best application of cluster analysis. The main contribution of this paper is to introduce a procedure for validating clusters. This procedure is an applicable technique to obtain reference models.

Target zone selection for hydraulic fracturing using sedimentological and rock mechanical studies with the support of the machine learning method of cluster analysis

Appropriate zone selection is crucial for hydraulic fracturing as it can significantly impact the success of the treatment and the overall cost. Many parameters affect true zone selection for hydraulic fracturing but lithology and geomechanical parameters are the most important. In this study, geological and geomechanical studies were incorporated with statistical approaches to determine geomechanical zones and intervals, which are brittle and can be candidates for hydraulic fracturing for the Sarchahan, Zakeen, and Faraghan formations (Silurian to Permian) in the deepest well in southern Iran Offshore. Based on sedimentological studies, five sedimentary facies were recognized: Sandstone, Shale, Red Mudstone, Mudstone, and Carbonate. In the next step, rock mechanical parameters were extracted from petrophysical logs and experimental tests. Six Rock Mechanical Facies (RMF) were determined based on main rock mechanical parameters including Young's modulus (E), Poisson's ratio (PR), uniaxial compressional strength, internal friction, and cohesion along with statistical clustering approaches. Then, nine geomechanical units (GMU), including GMU A to GMU I, were extracted from RMFs and were correlated with geological studies. Brittleness index (BI) was calculated based on E and PR and was correlated with variation of Al2O3 and SiO2 from XRF analyses. Brittleness index, GMUs rock mechanical properties, homogeneity of GMUs, and their geological characteristics are used to determine possible candidate GMUs for hydraulic fracturing. Three GMUs are recognized as the best GMUs for hydraulic fracturing in the studied well.

Performance Test, Index System Establishment, and Comprehensive Evaluation of Earthquake Rescue Robots

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for a comprehensive evaluation method that encompasses establishing an evaluation index system, testing performance indexes, and conducting performance evaluation. Firstly, four main criterion and twenty-three sub-criterion indexes are established by conducting a comprehensive review of existing assessment measures for rescue robots across diverse domains. These indexes are validated through test modules developed by the National Earthquake Response Support Service to obtain corresponding values for each criterion. Moreover, a method for establishing the index system is proposed based on the fuzzy clustering analysis and grey correlation analysis methods. This method effectively addresses issues related to excessive subjectivity, redundancy, and ambiguous stratification of indexes. Subsequently, the DEMATEL is employed to scrutinize the interrelationships and causal connections among each index within the established index system, leading to the identification of input and output indexes based on the analysis outcomes. Finally, as an empirical example, three earthquake rescue robots are comprehensively evaluated and ranked using the super efficiency DEA model. Alongside analyzing results regarding input redundancy and output deficiency, targeted improvement suggestions are provided for each earthquake rescue robot. Additionally, comparison analysis with the entropy weight method and VIKOR method verifies the effectiveness of our proposed method.

Headway regularity as an attribute for classifying bus drivers

Different indices have been proposed in the literature to characterize headway regularity. These metrics aggregate the headway variability for a service, but none can be directly associated with a specific driver. This paper seeks to understand drivers' influence on a service's regularity. To do so, we propose four regularity indices related to a driver's performance and use the Hierarchical Clustering Analysis method to generate a classification of drivers according to their contribution to the headway regularity during the operation of a service. We characterize each class based on the driver's attributes such as age, years of experience as a driver, and years in the bus company, and those attributes associated with the operation, such as number of services per day and period of the day. The results show consistency in the classification obtained, with nearly 90% of drivers remaining in the same regularity classes regardless of the index.

APPLICATION OF CLUSTERING METHODS FOR DETECTING NETWORK THREATS

The objective of this article is to explore the possibilities of identifying network attacks using cluster analysis methods. Density-based spatial clustering of applications with noise (DBSCAN) and the ordering points to identify the clustering structure (OPTICS) algorithm have been implemented on data representing a simulated network. The findings reveal the most effective algorithmic combinations for distinguishing between regular connections and data associated with network intrusions.

Spatiotemporal Variation and Causes of Typical Extreme Precipitation Events in Shandong Province over the Last 50 Years

In this study, based on hourly ERA5 reanalysis data from July to September, from 1971 to 2020, for Shandong Province, we used mathematical statistical analysis, the Mann–Kendall nonparametric statistical test, cluster analysis, and other methods to extract and analyze the spatiotemporal variation characteristics and causes of typical extreme precipitation events. The results indicated the following: (1) The total number and duration of precipitation events show a nonsignificant upward trend, while the average and extreme rainfall intensities show a nonsignificant downward trend. (2) Extreme precipitation events are primarily concentrated in Qingdao, Jinan, Heze, and Binzhou, with fewer events occurring in central Shandong Province. (3) Extreme precipitation events are classified into four types (namely, patterns I, II, III, and IV). Pattern I exhibits two rain peaks, with the primary rain peak occurring after the secondary rain peak. Similarly, pattern II also displays two rain peaks, with equivalent rainfall amounts for both peaks. In contrast, pattern III has multiple, evenly distributed rain peaks. Finally, pattern IV shows a rain peak during the first half of the precipitation event. Pattern I has the highest occurrence probability (46%), while pattern IV has the lowest (7%). (4) The spatial distributions of the different rain patterns are similar, with most being found in the eastern coastal and western regions. (5) Extreme precipitation events result from interactions between large-scale circulation configurations and mesoscale convective systems. The strong blocking situation and significant circulation transport at middle and low latitudes in East Asia, along with strong convergent uplift, abnormally high specific humidity, and high-water-vapor convergence centers, play crucial roles in supporting large-scale circulation systems and triggering mesoscale convective systems.

Analisis Struktur Cluster Pasar Industri pada Sektor Publik

This research aims to analyze the structure of industrial market clusters in the context of the public sector. The industrial market cluster structure refers to the pattern of relationships between companies in a sector that are interrelated in the aspects of production, distribution and marketing. This research uses a primary and secondary data analysis approach to identify patterns and relationships between entities in the public sector. Through the use of cluster analysis methods, this research describes the relationship between companies in the public sector based on characteristics such as industry specialization, geographic location, and marketing networks. The results of this analysis can provide valuable insights for policy makers and stakeholders in understanding the dynamics of industrial markets in the public sector as well as the potential for collaboration and competition between industry players.

Data-driven determination of zooplankton bioregions and robustness analysis

Identifying biogeographic regions through cluster analysis of species distribution data is a common method for partitioning ecosystems. Selecting the appropriate cluster analysis method requires a comparison of multiple algorithms. In this study, we demonstrate a data-driven process to select a method for bioregionalization based on community data and test its robustness to data variability following these steps:•We aggregated and curated zooplankton community observations from expeditions in the Northeast Pacific.•We determined the best bioregionalization approach by comparing nine cluster analysis methods using ten goodness of clustering indices.•We evaluated the robustness of the bioregionalization to different sources of sampling and taxonomic variability by comparing the bioregionalization of the overall dataset with bioregionalizations of subsets of the data.The K-means clustering of the log-chord transformed abundance was selected as the optimal method for bioregionalization of the zooplankton dataset. This clustering resulted in the emergence of four bioregions along the cross-shelf gradient: the Offshore, Deep Shelf, Nearshore, and Deep Fjord bioregions. The robustness analyses demonstrated that the bioregionalization was consistent despite variability in the spatial and temporal frequency of sampling, sampling methodology, and taxonomic coverage.

Digital Transformations of Public Administration in the Context of the COVID-19 Pandemic: EU Countries Case Study

The digital transformation of public administration and public services has become an urgent need for many governments around the world. This article aims to explore the homogeneity and pace of digital transformation of public administration through the impact of the COVID-19 pandemic and to identify problems and forecasts of research processes at the EU level. The method of cluster analysis was used to study the similarity of the digital transformation of public administration in the EU. The pace of digitalization of public administration in the context of the COVID-19 pandemic was studied through trend analysis. The results show that the EU countries, according to the level of public administration digitalization, are grouped into five clusters, between which there are significant digital gaps. The COVID-19 pandemic has widened the gaps, potentially impacting the EU’s strategy for digitalizing society and establishing e-government. Public e-services usage in 2020 to 2021 is higher than predicted by trend analysis for 2009 to 2019. This suggests an acceleration of digitalization in the EU during the COVID-19 pandemic. The proposed technique can be used to predict the level of digitization of any country or group of countries.

Factors Controlling Productivity – A Case Study on the Qixia Formation of the Shuangyushi Area, Sichuan Basin

The gas reservoir with high temperature and high pressure (HTHP) over 8,000 m is one of the carbonate gas reservoirs discovered in recent years in the Qixia Formation of the Shuangyushi area, NW Sichuan Basin, and the strong heterogeneity makes it difficult to deploy high-yield wells. The relationship between geology and productivity is given by using the research results of reservoir evaluation and statistical analysis of the dynamic analysis of trial production in the last 4 years in this paper. Then, the evaluation index system of high productivity controlling factors in the gas reservoir is given by using the fuzzy clustering analysis method. Finally, the relative importance of each index is quantitatively analyzed and evaluated by using the analytic hierarchy process to determine the key controlling factors affecting productivity. The evaluation results are highly consistent with the current characteristics of high-yield wells. The productivity of the newly completed Well 008 and Well 107 outside the trial mining area exceeded 1 million m3/day. The result shows that the high productivity of ultradeep gas wells in the Qixia Formation is mainly controlled by the degree of dolomitization, the distribution of high-energy shoal-mound complexes, and the degree of fracture development. It can be used to guide the deployment of productive wells in the southern area of the Shuangyushi-producing test area.