Use Of Cluster Analysis Research Articles

Assessment of the Impact of Commercial Banks’ Operating Activities on the Natural Environment by Use of Cluster Analysis

The aim of the paper is to identify groups of banks with similar environmental commitment, taking into account their direct environmental impact. The study, which employs the aggregation method, reveals that small banks with a relatively worse financial standing are characterised by the lowest level of disclosures within pro-ecological initiatives. At the same time, large international banks belong to clusters defined by the highest or the lowest disclosure rates. The above-mentioned phenomenon results from the dichotomy of integrating environmental policy into their strategies and business models. This study is the first comparative analysis of the extent to which all listed (and at the same time the biggest) banks operating in Poland have taken initiatives to reduce the negative environmental impact of their activities.

A phytoindicational assessment of the vegetation of afforestation belts in the Middle Dnipro Region, Ukraine

Abstract We performed a study of the vegetation of afforestation belts in the Middle Dnipro Region of Ukraine. These were planted to protect agricultural land from adverse climatic conditions. The syntaxonomy of these afforestation belts is, so far, embedded in the synanthropic class of Robinietea tree plantations, but over time, these may need another syntaxonomic solution. Based on the data, a phytoindicational assessment of these communities was performed. The ecological features of the vegetation of afforestation belts have been determined by the mutual influence of ecological factors of different origin, and the extent of these impacts. In particular, the climatic conditions and edaphic factors and also the peculiarities of the environment-forming influence of different dominant tree species in the main layers and the age of these plantations, which determines at different stages of sylvestral succession processes. According to our phytoindicational assessment, use of cluster analysis of these biotopes of afforestation belts, proved their considerable similarity taking into account all ecological factors, and the distribution is mainly due to the predominant tree species. As a result of ordination analysis, it was found that the factors that most correlate with the ordinal axes are light regime, soil nitrogen and humidity. Phytocoenoses of afforestation belts are a very specific component of the forest biome in this region, because they have been artificially created.

0757 Cluster Analysis for Identifying Good CPAP Adherence Using the PSG Parameters and Patient Characteristics

Abstract Introduction CPAP is the standard treatment for obstructive sleep apnea (OSA). One of the important clinical issues to be solved is poor CPAP adherence. A growing body of studies has identified predictive factors for CPAP adherence including AHI, BMI, age, gender, symptoms, etc. When our sleep physicians prescribe CPAP, we would consider these known factors in multiple ways. One may want to know factors' combinations rather than each factor. In this case, cluster analysis might be useful since it is a powerful data-mining tool to sort various factors into meaningful groups. Recently, cluster analysis has been adopted for research of sleep breathing disorders. However, no one has adopted to predict CPAP adherence. In this study, we aimed to explore the usefulness of cluster analysis to predict CPAP adherence using the diagnostic PSG parameters and patients’ characteristics. Methods The study design was a retrospective observational multi-center study including 5 certified sleep centers in Japan. For 2 years from 2017, 1133 patients who were diagnosed with OSA with in-lab PSG and newly initiated CPAP therapy were enrolled. We performed cluster analysis using the K-means clustering. Variables for clustering were determined by several sleep physicians among PSG parameters and patients’ characteristics. We assessed CPAP adherence for 90 days and 365 days after CPAP initiation in each created cluster. We adopted CMS criteria for good CPAP adherence, which is, more than four hours of use on 70% of nights. Results Cluster analysis classified 5 clusters. A significant difference in CPAP adherence for 90 days and 365 days was seen among 5 clusters with a test of independence (p=0.001, p=0.005, respectively). The cluster presenting moderate obese, very high AHI and ODI, and apnea predominant indicated good adherence, whereas the cluster presenting morbid obese, very high AHI and ODI, sustained severe hypoxia, younger age, and daytime sleepiness indicated poor adherence according to the post-hoc Chi-square test. Conclusion Cluster analysis successfully distinguished the different CPAP adherence and identified a combination of OSA patients’ profiles. Thus, cluster analysis would be a useful tool for predicting long-term CPAP adherence. Support (If Any)

Use of Cluster Analysis to Identify Subtypes of Pediatric Functional Nausea.

The aim of the study was to evaluate whether there are clinical subtypes in children with functional nausea based on comorbidities and responses to the Nausea Profile questionnaire. Patients from the Neurointestinal and Motility Program clinical registry at Lurie Children's Hospital were included if they met Rome IV criteria for functional nausea. Patients completed the Nausea Profile, a multidimensional measure of nausea with gastrointestinal, emotional, and somatic subscales. Comorbidities were assessed by chart review and self-report measures. Latent class analysis was used to identify patient groups based on comorbidities. To assess if model-identified groups were predictive of differences in nausea quality, Nausea Profile subscale means were compared between groups and used to predict group membership. Conversely, k-means analysis was used to divide the sample into groups based upon Nausea Profile subscale scores, to determine if identified groups had different comorbidities. Seventy-two patients (n = 53 girls) with a mean age (±SD) 14.5 ± 2.9 were included. Two clinical subtypes were identified based on comorbidities, with responses on the emotional subscale of the Nausea Profile predicting group membership (P < 0.04). When patients were grouped by nausea quality, the resulting clusters differed on psychiatric comorbidities (P < 0.001). Our findings support the existence of nausea subtypes within the broad diagnosis of functional nausea. One such subtype is an emotional predominant nausea supporting the notion that anxiety and depression constitute a subset of patients with nausea. Thus, patients may benefit from a treatment approach that integrates both GI assessment and psychiatric support in their care.

A evolução dos sistemas locais de ensino de nível médio no Brasil entre 1991 e 2010.

Abstract In spite of the negative overall representation of Brazilian secondary schools, the educational system at this level has shown remarkable quantitative developments. This paper proposes evolutionary paths of development of local secondary educational systems and addresses their determinants. It uses four schooling indicators - age specific enrollment ratio, gross enrollment ratio, net enrollment ratio, and age-grade distortion - conjointly with the use of cluster analysis to determine development trajectories. Furthermore, it analyses the main determinants of the evolutionary paths with the use of logistic and multinomial logistic models. Regional aspects related to socioeconomic level, labor market returns to human capital, demographics, social stability, location and microenvironment were associated with educational evolution.

MODELING OF PROCESSES OF FORMATION OF STARTUP ECOSYSTEMS ON THE BASIS OF CLUSTER ANALYSIS: ENTREPRENEURIAL ASPECT

The aim of the article is to model the processes of formation of startup ecosystems on the basis of cluster analysis as a basis for developing strategies for their development. The development of startup ecosystems is due to various support systems that are implemented at different levels: international, national, regional, local. The article considers the peculiarities of starting a startup ecosystem in Ukraine and abroad. It is proved that the topic of startups is quite popular in Ukrainian business, as startups are the largest number of innovative and innovative products or services, which is a significant prospect for attracting domestic and foreign investment in the economy. The main approaches to the definition of "startup" and "ecosystem" are analyzed. In a startup ecosystem, all actors involved are connected. Entrepreneurs come together to share ideas and interact with universities to attract future employees, and investors learn to understand which types of entrepreneurs, teams and startups are most likely to succeed and exit, investing in startups to raise new capital. Models of cluster analysis of processes of formation of startup ecosystems are constructed, which gave the chance to generalize ratings of ecosystems of the countries on 5 clusters. The purpose of cluster analysis is the formation of relatively homogeneous groups (clusters) in the space of variables based on a set of models and methods of aggregation of rows of the data matrix. The use of cluster analysis has a certain sequence of actions and involves the use of several methods. The grouping included 96 countries (4 countries that were included in the ranking for the first time were excluded automatically), which created 5 clusters that differ from each other, but have common characteristics within the selected groups. Each cluster is characterized by features. This division into clusters allowed to highlight the features of the development of startup ecosystems, provide characteristics of each group of countries and in the future will be the basis for developing recommendations and formulating strategies for the development of startup ecosystems.

Changes in the national composition of the Red Army in 1942-1945: multidimensional statistical analysis of data taking into account various categories of military personnel

The subject of the study in this article is the dynamics of changes in the national composition of various categories of the Red Army during the Great Patriotic War, the object of the study is the summary albums of socio—demographic data of the list of the Red Army, compiled in the period under review in the General Staff with a frequency of once every six months in a single copy as a generalizing reference material for the top leadership of the state and the armed forces. The purpose of this work is to analyze the changes in the national composition of various categories of Red Army servicemen (commanding officer (since 1943 - officer), junior commanding officer (since 1943 – sergeant), enlisted and cadet personnel) that occurred from mid-1942 to early 1945 (this period is determined by availability of source data). Based on the nature of the source under study, the research approach is based on the use of cluster analysis – one of the most well-known methods of multidimensional statistical analysis. The novelty of the study lies in the fact that for the first time, using cluster analysis, an analysis of statistics from albums of socio-demographic data of the Red Army roster during the Great Patriotic War, which became available to researchers only in 2017 and have not yet been fully introduced into scientific circulation, was carried out. The analysis revealed the main directions of changes in the national composition of the Red Army, in particular, general and special in the dynamics of the distribution of servicemen of various nationalities by military categories. The conducted research has shown that this source has a very high information potential for statistical research on changes in the national composition of the Red Army during the Great Patriotic War.

Use of Cluster Analysis to Group Organic Shale Gas Rocks by Hydrocarbon Generation Zones

In the last decade, exploration for unconventional hydrocarbon (shale gas) reservoirs has been carried out in Poland. The drilling of wells in prospective shale gas areas supplies numerous physicochemical measurements from rock and reservoir fluid samples. The objective of this paper is to present the method that has been developed for finding similarities between individual geological structures in terms of their hydrocarbon generation properties and hydrocarbon resources. The measurements and geochemical investigations of six wells located in the Ordovician, Silurian, and Cambrian formations of the Polish part of the East European Platform are used. Cluster analysis is used to compare and classify objects described by multiple attributes. The focus is on the issue of generating clusters that group samples within the gas, condensate, and oil windows. The vitrinite reflectance value (Ro) is adopted as the criterion for classifying individual samples into the respective windows. An additional issue was determining other characteristic geochemical properties of the samples classified into the selected clusters. Two variants of cluster analysis are applied—the furthest neighbor method and Ward’s method—which resulted in 10 and 11 clusters, respectively. Particular attention was paid to the mean Ro values (within each cluster), allowing the classification of samples from a given cluster into one of the windows (gas, condensate, or oil). Using these methods, the samples were effectively classified into individual windows, and their percentage share within the Silurian, Ordovician, and Cambrian units is determined.

A Pilot Exploratory Study to Form Subgroups Using Cluster Analysis of Family Needs Survey Scores for Providing Tailored Support to Parents Caring for a Population-Based Sample of 5-Year-Old Children with Developmental Concerns.

In a population-based developmental screening program, healthcare providers face a practical problem with respect to the formation of groups to efficiently address the needs of the parents whose children are screened positive. This small-scale pilot study explored the usefulness of cluster analysis to form type-specific support groups based on the Family Needs Survey (FNS) scores. All parents (N = 68), who accompanied their 5-year-old children to appointments for formal assessment and diagnostic interviews in the second phase of screening, completed the FNS as part of a developmental questionnaire package. The FNS scores of a full dataset (N = 55) without missing values were subjected to hierarchical and K-means cluster analyses. As the final solution, hierarchical clustering with a three-cluster solution was selected over K-means clustering because the hierarchical clustering solution produced three clusters that were similar in size and meaningful in each profile pattern: Cluster 1—high need for information and professional support (N = 20); Cluster 2—moderate need for information support (N = 16); Cluster 3—high need for information and moderate need for other support (N = 19). The range of cluster sizes was appropriate for managing and providing tailored services and support for each group. Thus, this pilot study demonstrated the utility of cluster analysis to classify parents into support groups, according to their needs.

Neutrosophic K-means for market segmentation

Markets may be broken down into subsets with the use of cluster analysis. Multivariate analytic methods are often used in traditional research. Due to their success in engineering, artificial neural systems have recently found use in business as well. When it comes to grouping observations with comparable traits or attributes, the K-means method is a common choice. It has various uses in marketing, but it finds particular success in cluster analyses of customer behavior. Several commercial packages include implementations of the K-means algorithm. Data mining statistical approaches like K-Means are useful for handling this data and analyzing it later on. For better results, this study combines the traditional K-Means technique with Neutrosophy, which accounts for the uncertainty inherent in such complicated data sets by factoring in the data's diversity and its inherent volatility as a result of proximity between the bounds of the separate segments as well as the members who make up each.

Use of Cluster Analysis to Characterise Aortic Stenosis Phenotypes with Treatable and Untreatable Risk

Use of Cluster Analysis to Characterise Aortic Stenosis Phenotypes with Treatable and Untreatable Risk

Staging Breast Cancer-Related Lymphedema with Bioimpedance Spectroscopy.

Background: A number of different classification schemes, with generally similar but not identical criteria, exist to describe the different stages of lymphedema. Criteria may include both subjective and objective assessments. The present study investigated whether bioelectrical impedance parameters had utility in staging breast cancer-related lymphedema. Methods and Results: Hierarchical agglomerative cluster analysis was used to assign women (n = 221) at risk of or with clinically ascribed lymphedema to clusters sharing similar impedance characteristics. Five clusters could be identified with each cluster containing proportions of participants that closely aligned with staging allocation, according to International Society of Lymphology criteria, at initial presentation. The use of cluster analysis for tracking of lymphedema progression or response to treatment is demonstrated. Conclusions: No single assessment provides definitive assignment of a patient to lymphedema stage. Staging is usually achieved by identifying and allocating a patient to a lymphedema stage shared by a group of patients with similar clinical signs. Cluster analysis of impedance data provides similar groupings of patients and could provide a useful adjunct objective assessment for staging lymphedema.

Cluster analysis of coffee blends for some sensory properties: a comparative approach to the ABIC’s classification criteria

In Brazil, coffee beverage quality is classified according to technical recommendations of the Associação Brasileira da Indústria de Café (ABIC), which determines cutoff points to discriminate from non-recommended to gourmet coffees. Accordingly, this study aimed to propose the use of cluster analysis to evaluate coffee blends composed of coffees with different qualities and of different varieties regarding a few sensory properties, using continuous and binary scales obtained by a cutoff, which defines whether the coffee is recommendable or not according to the ABIC criteria. It is believed, therefore, that this technique can be used to analyze coffee beverage quality as it is easily accessible and implemented by researchers. In conclusion, a qualitative cluster analysis using the minimum cutoff value of the ABIC had more promising results. This is because blends whose composition contained high and moderate proportions of specialty coffees were more homogeneous in the clustering.

Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree

Classic forecasting methods of natural gas consumption extrapolate trends from the past to subsequent periods of time. The paper presents a different approach that uses analogues to create long-term forecasts of the annual natural gas consumption. The energy intensity (energy consumption per dollar of Gross Domestic Product—GDP) and gas share in energy mix in some countries, usually more developed, are the starting point for forecasts of other countries in the later period. The novelty of the approach arises in the use of cluster analysis to create similar groups of countries and periods based on two indicators: energy intensity of GDP and share of natural gas consumption in the energy mix, and then the use of fuzzy decision trees for classifying countries in different years into clusters based on several other economic indicators. The final long-term forecasts are obtained with the use of fuzzy decision trees by combining the forecasts for different fuzzy sets made by the method of relative chain increments. The forecast accuracy of our method is higher than that of other benchmark methods. The proposed method may be an excellent tool for forecasting long-term territorial natural gas consumption for any administrative unit.

Using multivariate statistical methods to estimate the model maize population

In order to objectively assess the degree of similarity of varieties and hybrids by a set of indicators and to interpret a large amount of data in breeding practice, methods of multidimensional statistics are used, including factor and cluster analyzes. The study included 42 maize hybrids (FAO 100-499) of the selection of various scientific institutions of Russia, which were assessed according to the following characteristics: the length of the interphase stage of seedlings - flowering of panicles and ears, plant height, ear attachment height, grain yield, number of grains per ear, crude protein and crude fat content. Factor analysis made it possible to calculate the correlation coefficients between the variables under consideration and to reveal high positive correlations between the interfacial stages of a panicle seedlings–flowering and seedlings–-flowering of ears (r = 0.99), plant height and ear attachment height (r = 0, 81), the length of the interphase periods and the ear height (r = 0.77). A significant correlation coefficient was determined between the traits, the length of the interphase periods, seedlings – flowering of panicles and ears with grain yield (r = 0.34 and 0.36, respectively), grain yield and number of grains per ear (r = 0.37). When calculating the weights of the variables for the components, it was found out that the largest contribution (variance 54.814%) to the first hypothetical factor was made by the following signs: interphase periods of panicle seedlings–flowering and of ear seedlings–flowering, plant height, ear attachment height, grain yield, 1000 grain weight. The use of cluster analysis made it possible to group the studied hybrids at 32 iteration steps (Euclidean distance - 7.340) into 10 clusters with different breeding values. The hybrids that make up the clusters are characterized by a similar set of features within the clusters and significant differences between the clusters, confirmed by the method of unorganized repetitions of one-way ANOVA.

The use of cluster analysis to identify different burnout profiles among nurses and care assistants for older adults

The twofold purpose of this study was to identify specific burnout patterns among nurses and care assistants for older people, and to determine whether these profiles differ according to socioprofessional variables.A sample of 279 healthcare professionals completed an online survey comprising the Maslach Burnout Inventory and socioprofessional items. Cluster analysis identified five profiles on a burnout-engagement continuum. Participants with the Engaged profile had low scores on all three burnout dimensions (p < .001), while those with the Burnout profile had high scores on all three dimensions (p < .001). Three intermediate profiles were described. The Disconnected profile was characterized by higher depersonalization (p < .001), the Overextended profile by high emotion exhaustion (p < .001), and the Ineffective profile by low personal accomplishment (p < .001). Less experienced professional caregivers were more prone to burnout in terms of depersonalization (p < .01), while more experienced professional caregivers tended to have an ineffective burnout profile (p < .01). Identifying profiles of burnout symptoms can help to develop personalized interventions.

Digital Consumers in the Foodservices Market

The dynamic development of e-commerce in the foodservice market has been observed in recent years (especially during the COVID-19 pandemic), both in Poland and in other countries. The purpose of the study was to determine the characteristics of e-foodservice customers, identify their motivations for ordering food online and the nutritional consequences of such actions, as well as recognize the influence of consumers’ s-commerce activity on their foodservice market behavior. The study was carried out using the CAWI method on a sample of 1018 adult Poles. With the use of cluster analysis, three consumer segments were distinguished: frequent e-commerce users (221), moderate users (400), and sporadic users (397). The results indicate that frequent users of e-commerce in the foodservice market are slightly more often young and educated men who work full-time, have a higher than average income, and live in a large or medium city. In their own opinions, frequent users are the most concerned about proper nutrition, but despite such declarations, their nutritional quality is inferior to other groups. Compared to other segments, they are the most socially active and show the greatest commitment to s-commerce activity, which influences their purchasing intentions and behaviors.

Use of cluster analysis and analysis of the main components for evaluation of triticale samples

The study was conducted in the period 2015-2017 in the experimental field of IRGR "K. Malkov” Sadovo. The elements of productivity of 24 varieties and lines of triticale (X. Triticosecale Wittmacks) were studied. The degree of variation of each of the indicators of productivity is determined by calculating a coefficient of variation. The results obtained show that the most variable indicator is the weight of grains in the central spike whereas the slightest variation is observed in the parameters of test weight and 1000 grains weight. Hierarchical cluster analysis and analysis of the main components (PC-analysis) are applied to assess the genetic similarity and distance between the different varieties. As a result of the clustering the studied samples were divided into six groups. The varieties Trit 32/6 and KS 60 are characterized with the greatest genetic proximity, followed by line BGR 30071 with the Bulgarian variety Rozhen. The largest genetic difference was reported between line BGR 30816 and variety KT 81. The applied analysis of the main components shows that the components PC 1, PC 2 and PC 3 explain 70.6% of the total variation of all traits by genotypes. The largest numbers of samples (9) belong to component 3, as four of them are located in the negative values of PC 3, and the other five genotypes are in the positive values of PC 3. Component 1 is represented by eight samples, and component 2 includes seven triticale genotypes.

CLUSTER AND CONJOINT ANALYSIS FOR DETERMINING ONLINE SHOP SHOPEE CUSTOMERS PREFERENCE BASED ON E-SERVICE QUALITY

The use of e-commerce as a means of shopping is a trend that is very much in demand by many Indonesians. This makes e-service quality very important in a transaction. Customer preference is one of the main factors in shaping a business strategy. This research discusses the use of cluster analysis to segment Shopee's e-commerce customers based on sociodemographic characteristics with k-means algorithm and conjoint analysis to determine which e-service quality attributes are most important to each cluster. The sociodemographic characteristics to be analyzed are gender, education, profession, e-commerce visit, income, and last purchase from the e-commerce. The result from k-means algorithm is there are 2 groupings of customers based on their sociodemographic characteristics, cluster 1 with the majority of women members with frequent visits, while for cluster 2 with the majority of male members with frequent visits. With the result of cluster analysis, conjoint analysis help this research to find which e-service quality attributes are most important. The results are members in cluster 1 prioritize Full payment payment methods when shopping online, while members in cluster 2 prioritize star seller types when shopping online. The aspect that doesn't matter most when shopping online is fulfillment in cluster 1 and security in cluster 2.

Quantitative and qualitative analysis of the structure of a historical text

The subject of this research is the historical writing technique, which allows creating convincing images of the past. The goal of this article is ti analyze the structure of texts written by the historians and covering the period from antiquity to the XXI century. The theoretical framework consists of the idealistic approach towards history developed by the author. This article is first to examine the structure of texts written by the prominent historians of the past, such as Herodotus, Nestor, Karamzin, Klyuchevsky, and some historians of the XX &amp;ndash; XX centuries from the perspective of idealistic approach and the use of quantitative methods. For comparison, analysis is conducted on the literary texts of A. S. Pushkin and V. S. Pikul dedicated to historical themes. The article employs content analysis, structural analysis, and terarchical cluster analysis of the texts on the basis of their structure. This revealed that the structure of these texts consists of the factual and theoretical statements, where the firs prevailed until the mid XX century. The use of cluster analysis allowed building a matrix of similarity of the works. The main method of creating convincing historical text lies in selection and interpretation of the the facts in accordance with the dominant worldview or a widespread historical concept. Facts are subordinated to the theory and confirm the fundamental ideas and historical concepts, as well as depict a convincing image of the past. The conducted analysis indicates that theory plays the key role in creating a convincing historical text, while facts are secondary; no significant impact of historical methods is revealed. The major difference between the analyzed historical and literary texts consists in the fact that there is no theory in the literary works.