Ward's Hierarchical Clustering Method Research Articles

Corporate Debt Dynamics: Sectoral Clustering Analysis Using NACE Classification in Slovakia

Research background: Many authors are currently exploring the impact of the industry on the financial structure of enterprises since there are statistically significant differences across various sectors, exposing the industry as a critical factor influencing corporate indebtedness. Clusters of sectors with homogeneous patterns of indebtedness and comparable levels of debt in various economic conditions may be determined, and, therefore, firms and their debt levels should be systematically examined and evaluated. Purpose of the article: The main aim of this paper is to evaluate the level of debt in the Slovak environment on a sample of 19,480 firms from various sectors and to identify the relationships among them to identify sectors with homogeneous patterns of indebtedness and, consequently, comprehend which sectors are the most stable and independent. Methods: Because NACE classification provides a framework for gathering and presenting statistical data based on many economic sectors, the number of input data was reduced based on cluster analysis. Using Ward's hierarchical clustering method using squared Euclidean distance, selected indebtedness ratios were used to define the sectors with comparable debt levels. To determine if there were statistically significant differences between the calculated debt ratios related to the economic sector, the Kruskal-Wallis test was performed. Subsequently, as the results indicated significant differences across various indebtedness ratios, a post hoc analysis was performed. Findings & Value added: A group of NACE activities in which enterprises are sufficiently comparable that it is appropriate to evaluate their debt levels using the chosen indebtedness ratios are considered to be in sectors C, F, G and H, which included to tertiary sector, while the sectors K, R and S may also be grouped in one cluster, while form the secondary sector. The key relevance of our findings is the benchmarking of selected sectors about indebtedness, which may be used to further examine their growth in each of the V4 nations, which is an essential area for the evolution of the European economy as a whole. Studies considering a relatively significant amount of capital structure determinants may be beneficial to owners and managers, regulators, and financial institutions since debt policy affects firm performance, value, and survival.

Selection and Yield Formation Characteristics of Dry Direct Seeding Rice in Northeast China.

Dry direct seeding rice (DSR) is an emerging production system because of increasing labor and water scarcity in rice cultivation. The limited availability of rice cultivars suitable for dry direct seeding hampers the widespread adoption of this cultivation method in Northeast China. This study aimed to investigate grain production and plant characteristics associated with dry direct seeding rice. We conducted a field experiment on 79 japonica rice cultivars in Shenyang City, Liaoning Province, Northeast China, in 2020 and 2021. This study found that the grain yield of the tested rice cultivars ranged from 5.75-11.00 t ha-1, with a growth duration lasting between 144-161 days across the cultivars. These cultivars were then categorized into high yielding (HY), medium yielding (MY), and low yielding (LY) based on daily yield by using Ward's hierarchical clustering method. The higher grain yield for HY compared to MY and LY was attributed to more spikelets per unit area. The HY alleviated the conflict between higher panicle density and larger panicle size by improving the seedling emergence rate and productive stem rate. It also significantly increased shoot biomass at maturity. The HY reduced the period between seeding and beginning of heading (BBCH 51) and the proportion of dry matter partitioned to the leaf at the heading stage. However, it also increased the accumulation of dry matter in the grain and the proportion of dry matter partitioned to the grain at maturity. Furthermore, the HY markedly increased the harvest index and grain-leaf ratio, which are beneficial to coordinate the source-sink relationship. A quadratic function predicted that 98 days is the optimum growth duration before heading (BBCH 51) for achieving maximum yield. In conclusion, for dry direct seeding rice, it is appropriate to select high-yielding japonica inbred rice cultivars with shorter growth duration before heading (about 93-102 day), higher panicle number (about 450-500 × 104 ha-1), more spikelet number per panicle (about 110-130), higher seedling emergence rate (about 70-75%), higher productive stem rate (about 60-70%), and greater harvest index (about 50-55%).

What do hospital-based health professionals need and expect from an mHealth app to support the first 1000 days of life? Results from a cross sectional study.

Several attempts have been made to introduce mHealth solutions to support maternal, newborn, and child health (MNCH). However, most of the available apps do not meet the needs of end-users, underscoring the urgency of involving them in the co-design of telehealth interventions. With this in mind, we investigated the needs and expectations of hospital-based health professionals (i.e., secondary users) providing care to pregnant women and new mothers with their babies for a hypothetical mHealth app to support the first 1000 days of life. From November 2021 to March 2022, we surveyed health professionals using a questionnaire that explored the perceived importance of specific content, functionalities, and technical features of the proposed app. We also collected sociodemographic information from secondary users. We performed descriptive analysis and then used Ward hierarchical clustering method to classify respondents according to their response patterns. We recorded the needs and expectations of 145 hospital-based health professionals from obstetrics/gynecology, nursery/neonatology, and pediatrics. We found general agreement with the proposed content of the app, particularly general information about health during pregnancy (92%) and potential risky infections during pregnancy (91%). Three clusters emerged from the analysis, with the high and medium demanding clusters rating the importance of app content and technical features as very high and high, respectively, while low demanding cluster expressing more skepticism, especially about some of the proposed functionalities of the app. Assessing the needs and expectations of end-users is an essential process for developing tailored and effective mHealth solutions. This study has shown that hospital-based health professionals generally recognize the value of the proposed app, suggesting their propensity to integrate such a telehealth solution into mainstream clinical practice.

Ward Hierarchical Clustering: Indonesia's Green Economy Implementation Performance based on Environmental Quality towards Sustainable Development Goals

Environmental issues have recently emerged as an essential topic of discussion on a global scale. Actions to protect and preserve the environment should be addressed, which causes several problems such as water and air pollution, soil degradation, forest destruction, and diversion of agricultural land. Economic goals are given higher priority than other goals, such as environmental and community goals, in national development strategies and programs. The "green economy" concept is a solution to the sustainable concept to overcome the abovechallenges. Thus, this paper attempts to build a sustainable economic model based on research from various scientific sources. The indicators presented in this paper can help advise regional policymakers to continue progressing and developing according tothe Sustainable Development Goals (SDGs) pillars. This study uses the Ward Hierarchical Clustering method. The first cluster is the provinces of Aceh, North Sumatra, and West Sumatra, which also interpret that the performance of implementing a green economy in related areas based on environmental quality is very good. The second cluster is East Java, South Sumatra, and South Sulawesi, which indicates that the performance of the implementation of a green economy in related areas based on environmental quality is quite good. Then the third cluster is West Java, which interprets that the performance of implementing a green economy in related areas based on environmental quality still needs attention from the local government. It is known that 97.06% of Indonesia's territory has implemented a green economy in related areas based on very good environmental quality.

Onset and demise dates of the rainy season in the South American monsoon region: A cluster analysis result

AbstractThis work proposes an adaptation of the method developed in previous papers to determine the onset and demise of the rainy season (ONR and DER) dates in the areas of the South American monsoon system (SAMS) based on the pentad antisymmetric outgoing long‐wave radiation (AOLR). In those papers, the sign change of the mean AOLR in the central Amazon Basin (CAM) and western central Brazil (WCB) from positive to negative defined the ONR, from negative to positive, the DER dates. Since the monsoon convection presents a northwest–southeast oriented progression, the antisymmetric area to the WCB was selected subjectively. Thus, here we propose to use the Ward hierarchical clustering method to select areas in the SAMS and in the northern tropical America (NTA) for the regionalized AOLR calculation. The significant (at the 95% confidence level) negative correlations with the largest magnitude among the clusters in the SAMS and NTA and the outgoing long‐wave radiation (OLR) and precipitation annual cycles in each group define the pairs to calculate the AOLR. Then, the AOLR time series is calculated and subjected to a 5‐pentad running mean filter. This method keeps the climatological features of the convection annual cycle such that the closer (farther) the pair is to the equator the longer (shorter) the rainy season. The ONR and DER dates found with this new method are remarkably close to those found previously. Therefore, the new method proposed here highlights regional aspects of rainy season and can easily be automatized for its routine application at the operational climate monitoring centres, for instance at INPE. This is the most important advantage of the method and might be relevant to the SAMS rainy season monitoring.

Health literacy profiling in persons with psoriasis - A cluster analysis.

ObjectiveTo explore health literacy (HL) profiles within a cohort of people with psoriasis. A cluster approach identifies groups of individuals that have similar HL profiles. The method unmasks sub‐groups with particular HL strengths, or subgroups with limitations, which require tailored healthcare services to improve.MethodsA cross‐sectional sample of 792 patients from the Norwegian Climate Helio Therapy Programme in Gran Canaria participated. The HL questionnaire assessed nine HL dimensions. Using Ward's Hierarchical Clustering Method (Stata version 16), we looked for subgroups of patients across the dimensions. We also explored whether these clusters had specific demographic features and associations to outcomes such as psoriasis knowledge, quality of life and self‐management capacity.ResultThe analysis revealed four unique clusters identifying clinically meaningful subgroups. Two groups stood out as especially interesting. One cluster representing 26.6% of the sample presented severe HL limitations associated with lower psoriasis knowledge, quality of life, self‐management and self‐efficacy. HL domains connected to cooperation with healthcare professionals showed deficient scores. The other cluster included a smaller percentage (7.7%) with high HL compared to the total sample. This cluster was associated with higher self‐management, quality of life and better self‐efficacy.ConclusionThe cluster analysis revealed substantial differences in HL profiles within the sample. These results support the importance of a holistic understanding of the HL needs and the vulnerabilities within a psoriasis cohort. Implementing one size fits all approaches, may not be sufficient in psoriasis context to target HL.

The determinants of COVID-19 case fatality rate (CFR) in the Italian regions and provinces: An analysis of environmental, demographic, and healthcare factors

The Italian government has been one of the most responsive to COVID-2019 emergency, through the adoption of quick and increasingly stringent measures to contain the outbreak. Despite this, Italy has suffered a huge human and social cost, especially in Lombardy. The aim of this paper is dual: i) first, to investigate the reasons of the case fatality rate (CFR) differences across Italian 20 regions and 107 provinces, using a multivariate OLS regression approach; and ii) second, to build a “taxonomy” of provinces with similar mortality risk of COVID-19, by using the Ward's hierarchical agglomerative clustering method. I considered health system metrics, environmental pollution, climatic conditions, demographic variables, and three ad hoc indexes that represent the health system saturation. The results showed that overall health care efficiency, physician density, and average temperature helped to reduce the CFR. By the contrary, population aged 70 and above, car and firm density, air pollutants concentrations (NO2, O3, PM10, and PM2.5), relative average humidity, COVID-19 prevalence, and all three indexes of health system saturation were positively associated with the CFR. Population density, social vertical integration, and altitude were not statistically significant. In particular, the risk of dying increases with age, as 90 years old and above had a three-fold greater risk than the 80–to–89 years old and four-fold greater risk than 70–to–79 years old. Moreover, the cluster analysis showed that the highest mortality risk was concentrated in the north of the country, while the lowest risk was associated with southern provinces. Finally, since prevalence and health system saturation indexes played the most important role in explaining the CFR variability, a significant part of the latter may have been caused by the massive stress of the Italian health system.

PENGGEROMBOLAN DERET WAKTU DENGAN PENDEKATAN UKURAN KEMIRIPAN PICCOLO UNTUK PERAMALAN CURAH HUJAN PROVINSI BANTEN

Time series data modeling can be done by modeling each object one by one. Monthly rainfall data is an example of time series data. The purpose of time series analysis is to find patterns of past data and then forecast the future characteristics of data. The data used in this study is the Banten Province rainfall data which contained 19 rainfall stations. So it will require 19 models to forecast the rainfall data. The pattern of time series data in Banten Province monthly rainfall data in several locations has similarities. So that the similarity of this pattern can be considered in the clusters. In time series clustering, the idea is to investigate the similarity of time series in a cluster. The accuracy of distance similarity size measurements is performed on the generation data generated from 3 models, namely AR (1), AR (2), and AR (3). The piccolo method has an average accuracy of 0.62. While the maharaj method has an average accuracy of 0.41. This means that the Ward hierarchical clustering method using the Piccolo distance approach has a greater accuracy value than the Maharaj distance approach. Furthermore, the Piccolo method can be used as an alternative to the excellent distance method for grouping time series data in case data. The Banten Province rainfall station has 3 optimal clusters. Modeling individual level and cluster level has accuracy values that are not much different.

Assessment of Turkish consumer attitudes using an Animal Welfare Attitude Scale (AWAS)

The aim of this study was to examine Turkish consumer attitudes towards animal welfare in terms of cognitive, affective and behavioral dimensions, using a bespoke Animal Welfare Attitude Scale (AWAS). An overall consumer attitude was also determined. The Delphi technique was used to establish an item pool to develop a questionnaire for the construction of the AWAS. This questionnaire was later used for data collection. A total of 2295 consumers were surveyed in 14 cities, in the 7 regions of Turkey. Descriptive statistics, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), reliability analysis, Ward's hierarchical clustering method and One-way ANOVA were used to validate the questionnaire, and to analyze data. Results of the EFA allowed for allocation of 42 items collected under 3 dimensions (cognitive, affective and behavioral), that explained 72% of the total variance of the model. This factor structure was subsequently confirmed by a CFA performed on a different sample of 425 consumers. The Cronbach's Alpha coefficient for AWAS was calculated at 0.829. These results confirmed that the developed AWAS had a valid and reliable scale. The questionnaire showed that consumers' attitudes towards animal welfare were more negative at the behavioral dimension, than either at the cognitive or affective dimensions. Consumers in Turkey were ultimately divided into three groups according to their overall attitudes towards animal welfare as impassive, moderate or sensitive. One-third of Turkish consumers placed in the sensitive group, thus emphasizing a potential niche for animal-friendly food marketing in Turkey.

Variation of Phytoestrogen Content and Major Agronomic Traits in Alfalfa (Medicago sativa L.) Populations

Alfalfa is the main perennial legume and a rich source of phytoestrogens. Variation of phytoestrogen content and agronomic traits was evaluated in twenty alfalfa populations over two consecutive years. The most dominant phytoestrogen in alfalfa populations was genistein, while slightly lower levels were found for kaempferol and coumestrol. Several populations (Af 1, 12, 18, 19, 20) showed superior agronomic performance, especially high forage yield (up to 71.93 t ha−1). These populations were also characterized by a lower (Af 1, 12, 18, 20) or high (Af 1, 19) content of total and individual phytoestrogens, including Af 8 population with the highest total content (2637.10 μg g-1 of DM) and content of most of the individual phytoestrogens and favorable agronomic properties. Ward hierarchical clustering method grouped the twenty evaluated populations into four distinct clusters. Identified materials provide a valuable source of germplasm for further breeding programs in order to develop new high-yielding cultivars with either low (for forage use) or high (for possible pharmaceuticals/nutraceuticals industries) phytoestrogen content.

What Ward's clustering method tells about the four largest emerging equity markets

Investing in stock market indices or ETFs could be more reasonable (and secure) for relatively new and incognisant investors who are personally unable to value of each stock–firm in a way. This paper attempts to group the well-known four largest emerging stock markets so-called BRIC or big-four economies namely Brazil, Russia, India and China based on return characteristics through Ward's hierarchical clustering method over the period of 2005 and 2015. Additionally, the first principle component (PCA) of the related indices is calculated and the abnormal variability is observed through control chart over time. Results indicate that Brazilian and Indian markets show more similarity over the studied period.

What Ward's clustering method tells about the four largest emerging equity markets

Investing in stock market indices or ETFs could be more reasonable (and secure) for relatively new and incognisant investors who are personally unable to value of each stock–firm in a way. This paper attempts to group the well-known four largest emerging stock markets so-called BRIC or big-four economies namely Brazil, Russia, India and China based on return characteristics through Ward's hierarchical clustering method over the period of 2005 and 2015. Additionally, the first principle component (PCA) of the related indices is calculated and the abnormal variability is observed through control chart over time. Results indicate that Brazilian and Indian markets show more similarity over the studied period.

Variance-based Features for Keyword Extraction in Persian and English Text Documents

This paper address automatic keyword extraction in Persian and English text documents. Generally, for keyword extraction in a text, a weight is assigned to each token and words having higher weights are selected as the keywords. We have proposed four methods for weighting the words and have compared these methods with five previous weighting techniques. The previous methods used in this paper are term frequency (TF), term frequency inverse document frequency (TF-IDF), variance, discriminative feature selection (DFS), and document length normalization based on unit words (LNU). The proposed weighting methods are based on using variance features and include variance to TF-IDF ratio, variance to TF ratio, the intersection of TF and variance, and the intersection of variance and IDF. For evaluation, the documents are clustered using the extracted keywords as feature vectors, and K-means, expectation maximization (EM), and Ward hierarchical clustering methods. The entropy of the clusters and pre-defined classes of the documents are used as the evaluation metric. For the evaluations, we have collected and labelled Persian documents. Results show that our proposed weighting method, variance to TF ratio, has the best performance for Persian. Also, the best entropy is resulted by variance to TD-IDF ratio for English.

Vertical Wind Profile Characterization and Identification of Patterns Based on a Shape Clustering Algorithm

Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. This paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on $z$ -normalization, shape-based distance metric solution, and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using commercial Windcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100 000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes.

National fiscal frameworks in the post-crisis European Union

Research background: The latest economic and financial crisis has seriously injured European Union Member States, affecting the condition of their public finances. In the face of the crisis, the EU made a special effort to increase the effectiveness of national fiscal frameworks, e.g. by improving the compliance with legislation. The post 2009 reforms were aimed at providing a solid economic foundation for the national fiscal frameworks, especially in the high-debt euro area countries.
 Purpose of the article: The goal of this research is twofold. Firstly, it aims to provide an outline of the national fiscal governance in the EU. Secondly, the paper analyzes the changes in the core measures of fiscal governance in the EU between the crisis period and the year 2016 (due to the latest available data) and investigates the similarities in the progress made by the 28 EU countries in restoring balance in public finance.
 Methods: To achieve the goal, the literature review and the analysis of core elements of national fiscal frameworks are provided. In the empirical section the grouping method for all 28 EU countries based on the Ward's agglomerative hierarchical clustering method is employed. The study uses data derived from the AMECO database (in the case of fiscal data) and the European Commission thematic data for quality indexes of particular elements of fiscal governance (numerical fiscal rules, medium-term budgetary frameworks and independent fiscal institutions).
 Findings & Value added: This paper contributes to the literature by, on the one hand, attempting to analyze changes in main fiscal governance measures and, on the other hand, by assessing their link with public finance through employment of the agglomerative clustering method. Based on the results, the conclusion about the importance of the improvement in fiscal frameworks is provided. The analysis shows that countries with better national fiscal framework achieved better results in public finances regardless the macroeconomic conditions.

Synoptic conditions leading to extremely warm periods in Western Iran

ABSTRACTThe purpose of this study is the characterization of the synoptic circulation patterns leading to extremely warm periods (EWPs) over the Kurdistan province of Iran. Daily maximum temperature data from 27 meteorological stations were used for the period 1962–2012. Data were interpolated to a 6 × 6 km grid using the geospatial Kriging method. EWPs were defined as a period of at least 6 consecutive days with maximum temperature above the local daily‐based 95th percentile of the 1971–2000 period, with additional criteria for spatial extension. The identified EWPs are distributed through the whole year, with maximum frequencies in winter and summer. Interestingly, the decade 1988–1997 did not record any extreme episode and summer extremes were recorded only before 1983. The associated synoptic patterns were classified into four clusters by applying the Ward's hierarchical clustering method to sea level pressure and Z500 data. Four clusters are identified: two represent warm periods in winter, one those occurring spring, especially in March, and another one mainly reflecting summer heat waves. In general, it is found that EWPs are associated with statistically significant anomalies in regional circulation. A deep trough of geopotential height in 500 hPa over North Africa and a ridge directly over the study area were the more important causes for the occurrence of EWPs. Other synoptic configurations that favour extreme periods in winter and summer include, respectively, the northward expansion of the Sudan low pressure with an associated southerly wind advection and the persistence of anticyclonic circulation at all tropospheric levels enhancing subsidence and adiabatic heating. The spring pattern is the most frequent one (nearly 48% of all cases) and reflects a southeastward extension of the subpolar low pressure that leads to the displacement of the Siberian high to south and southwestern Asia and the accompanied northward expansion of the Sudan low.

JCES01.04 Liquid Biopsy in Monitoring Dynamic Changes of Driver Genes in Advanced NSCLC

JCES01.04 Liquid Biopsy in Monitoring Dynamic Changes of Driver Genes in Advanced NSCLC

Association of Polysensitization, Allergic Multimorbidity, and Allergy Severity: A Cross-Sectional Study of School Children

Background: Aeroallergen sensitization is related to the coexistence of allergic diseases, but the nature of this relationship is poorly understood. The aim of this study was to clarify the relationship of polysensitization with allergic multimorbidities and the severity of allergic diseases. Methods: This study is a cross-sectional analysis of 3,368 Korean children aged 6-7 years-old. We defined IgE-mediated allergic diseases based on structured questionnaires, and classified the sensitivity to 18 aeroallergens by logistic regression and the Ward hierarchical clustering method. The relationship of polysensitization (positive IgE responses against 2 or more aeroallergens classes) with allergic multimorbidities (coexistence of 2 or more of the following allergic diseases: asthma, rhinitis, eczema, and conjunctivitis) and severity of allergic diseases was determined by ordinal logistic regression analysis. Results: The rate of polysensitization was 13.6% (n = 458, 95% CI 12.4-14.8) and that of allergic multimorbidity was 23.5% (n = 790, 95% CI 22.0-24.9). Children sensitized to more aeroallergens tended to have more allergic diseases (rho = 0.248, p < 0.001), although the agreement between polysensitization and multimorbidity was poor (kappa = 0.11, p < 0.001). The number allergen classes to which a child was sensitized increased the risk of wheezing attacks (1 allergen: adjusted odds ratio [aOR] 2.22, 4 or more allergens: aOR 9.39), absence from school (1 allergen: aOR 1.96, 3 allergens: aOR 2.08), and severity of nasal symptoms (1 allergen: aOR 1.61, 4 or more allergens: aOR 4.38). Conclusion: Polysensitization was weakly related to multimorbidity. However, the number of allergens to which a child is sensitized is related to the severity of IgE-mediated symptoms.

Who will pass? Analyzing learner behaviors in MOOCs.

Massive open online courses (MOOCs) have recently gained worldwide attention from educational institutes. MOOCs provide a new option for learning, yet measurable learning benefits of MOOCs still need to be investigated. Collecting data of three MOOCs at Yuan Ze University (YZU), this paper intended to classify learning behaviors among 1489 students on the MOOC platform at YZU. This study further examined learning outcomes in MOOCs by different types of learners. The Ward’s hierarchical and k-means non-hierarchical clustering methods were employed to classify types of learners’ behavior while they engaged in learning activities on the MOOC platform. Three types of MOOC learners were classified—active learner, passive learner, and bystander. Active learners who submitted assignments on time and frequently watched lecture videos showed a higher completion rate and a better grade in the course. MOOC learners who participated in online discussion forum reported a higher rate of passing the course and a better score than those inactive classmates. The finding of this study suggested that the first 2 weeks was a critical point of time to retain students in MOOCs. MOOC instructors need to carefully design course and detect risk behaviors of students in early of the classes to prevent students from dropping out of the course. The feature design of discussion forum is to provide peer interaction and facilitate online learning. Our results suggested that timely feedback by instructors or facilitators on discussion forum could enhance students’ engagement in MOOCs.

Levels of macroelements and toxic elements in herbal teas

A total of 14 diverse herbal teas were examined, including: yarrow, basil, St.John?s wort, peppermint, horsetail, nettle, thyme, corn silk, hibiscus, marshmallow, chamomile, rosehip, heather and wild mint. The samples were prepared using the method of microwave digestion, and measurements were performed by the use of inductively coupled plasma mass spectrometry (Cd, Hg, Pb, As, Sb); atomic absorption spectrometry (Mg); emission flame photometry (Ca, K, Na) and spectrophotometry (P). Intervals of variation (mg/kg) for macroelements were: Ca (2738-35399); P (1545-6264); Mg (1647-7816); Na (293-10629) and K (9683-33985), and for toxic elements: Cd (0.014-0.645); Hg (&lt;0.001- 0.017); Pb (0.064-1.340); As (0.030-0.544) and Sb (0.004-0.068). In the three samples (yarrow and two St.John?s wort samples) measured cadmium concentration was higher than the maximum level for dried herbs, recommended by WHO. Ward's hierarchical clustering method was performed with the aim of grouping herbal teas by the amount of toxic elements.