hierarchical clustering

Earth surface temperature changes can affect various environmental factors and elements, and by affecting bioclimatic comfort, energy consumption, plant and animal phenology, air pollutant concentration, soil and air moisture density, and other environmental factors can also be affected by these factors.In this research, in order to investigate the role of actual evaporation-transpiration on the temperature of the earth's surface at ten meters, the variables of carbon monoxide, water vapor density, nitrogen dioxide, sulfur dioxide, ozone, water vapor pressure and wind speed in twelve uses With the help of LCZ algorithm, determined for Tehran and Alborz provinces, have been studied and investigated.The studied area has a high potential for the occurrence of thermal islands due to special geographical conditions such as location, variety of man-made structures and special ecological conditions. In this research, according to the impact of the studied variables and the need to examine the relationships between them, hierarchical clustering in a combined manner, path analysis method and remote sensing techniques have been used. According to the obtained results, among the studied variables, actual evaporation-transpiration in none of the uses had a significant effect on increasing or decreasing the temperature of the ground surface at 10 meters. Water vapor pressure in class 14 (areas with grassy vegetation) shows the greatest effect in increasing the temperature of the earth's surface.Based on the obtained results, the type of use is highly dependent on the impact of the studied variables on the surface temperature of the earth.

Co-word analysis is a tool to depict the intellectual structure of scientific productions. So the purpose of this study is to map the intellectual structure of research status in the field of educational management in Iran.

This applied research has been done by co-word analysis. The research population includes all original Persian scientific articles indexed in databases, named Normagz, SID, and Magiran. Examples with the four main keywords "educational management", "educational leadership", "school management" and "school leadership" along with their sub-topics and their plural and singular states based on searching for the same phrase and without applying a time limit, during the years 1988 to 2020, between 2nd and 20th of July 2020 were searched.  These keywords were selected according to "Eric Online Thesaurus", "Persian subject headings", "Congress library subject headings", "Educational management culture book" and "consensus of three professors of educational management". To collect data, first, 1066 articles were retrieved by an advanced search of selected keywords, and 891 articles remained after removing duplicate and unrelated items. In the next step, all the keywords of the articles that were under each title of the article were placed in the Excel file in one column, and the keywords in the titles of the articles were examined one by one by the researcher and after getting the final opinion from the experts in the field and they were given in the continuation of keyword column. At first, 3688 keywords including their repetitions (2221 words excluding repetitions) were obtained from the titles and keywords of the articles. After correcting the typographical and space errors that were too much due to the entry of relatively inappropriate information in Persian databases, the phase of assimilation of words began. In the next step, while checking the keywords one by one, referring to the accepted and common keywords (including 56 keywords) with the opinion of experts in the subject area of the research. Finally, a list of keywords was obtained in which all the words with semantic connections were replaced with the general keyword. A number of keywords completely unrelated to the subject area were also removed based on the opinion of subject experts in the field of educational management. Finally, the number of 2203 keywords, including their repetition, remained for further analysis. Afterwards, the researcher replaced the selected and referred keywords in the entire research data. Excel, RavaR Matrix, and UciNet software were used for data analysis and NetDraw was used to visualize the co-word network.

"Educational Management", "Educational Leadership" and "School Management" have been the most common themes in terms of co-word. In fact, these words form the main discussions related to this field. Hierarchical clustering resulted in the formation of 3 clusters in that the educational management cluster has a central and important position as a mature and developed cluster. Educational Leadership and School Management were identified as underdeveloped and emerging or declining clusters and compared to the educational management cluster, they have attracted less attention in the studies of the educational management field.

The study of the knowledge structure in the field of educational management identified the research status that can be a roadmap for future research. In the field of educational management, the issues surrounding learning leadership and school management as immature and underdeveloped clusters need more attention in future research. Therefore, if attention to these issues is placed on the agenda of the country's educational system, we will see more prosperity in the field of educational management in the future. Therefore, pushing the scientific community toward these issues and conducting research in this direction will be practical and fruitful.

Segmentation of International Markets of Saffron with Using Hierarchical Clustering TechniqueThe purpose of this study is segment of saffron product in different countries of the world on basis macro indicators selected in four demographic, economic, commercial and communication domains. research is a descriptive-correlational one and based on library studies. Selected communities include all UN member states and sample countries with populations above 500,000 or per capita income above $ 10,000 per year plus Getting into a strategic area constitutes a total of 143 countries used in the analysis. Hierarchical clustering method was used for data analysis by SPSS 25 software. The results show that there are eight clusters, with the highest number being in the second cluster with 100 countries and the lowest in the United States alone. Also, based on demographic results, the fifth, eighth and fourth clusters, which include China, India, the US, Brazil, Indonesia, Nigeria and Pakistan, can be attractive clusters in targeting saffron stakeholders in market.

IntroductionChange detection (CD) from remote sensing image considered an important topic among scientist because of its application in monitoring urban and non-urban area, environmental issue, damage assessment, etc. Presenting an efficient CD method from a high-resolution image can face a different challenge; most of the CD method from a high-resolution image requires training procedure to overcome this challenge. In this paper, an unsupervised (without needing training process) CD algorithm proposed from the high-resolution image. In this method spatial and spectral features extracted from bi-temporal images of the studied area. Difference images generated from high information content features. Then generated different images mapped into spherical space. The Primary change map created using implemented multi-thresholding method on created spherical space and the second change map created using hierarchical clustering regularized by Markov random field method. The final change map created by integrating the result of primary and secondary change maps. The final change map shows an overall accuracy of 92.56% in the studied area. Data and methodsThe data used in this paper is a subset of the main data with dimensions of 2000 * 2000 from an urban area in the city of Mashhad. These images corresponded to the two periods of 1390 and 1395 and were taken with UAV. The orthoimage is related to the first time with a spatial resolution of 6 cm and the second image is taken with a pixel size of 10 cm. In this paper, in order to detect of change of high-resolution images, first, the input images are registered in terms of spectral and spatial, and then feature images are extracted from each input image separately. In the next step, the differences images corresponding to high information content feature images are calculated. . The optimal difference images are mapped to the spherical space using selected statistical methods and in order to better analysis of the results. Otsu multi-thresholding method implemented on r component of sphere space. In the next step, the optimal difference image mapped to a spherical space is divided into non-overlapping blocks with the same dimensions; a cumulative hierarchical clustering method is applied for each block separately. In this case, the computational volume and space proposed in the hierarchical clustering method are reduced. The results of the cumulative clustering of the blocks are merged together and then the Markov random field method is used in order to regularize the results of the cluster in order to reduce noise.In final clustering, the class values below the lowest Otsu threshold are known as unchanged pixels with high reliability and the values above the maximum threshold are determined as changed pixels. The class of middle interval is unknown. For determining, the class of middle interval the corresponded output of hierarchy clustering regularized with a random Markov field is used. In the last step, a vegetation and shadow mask is used for final post-processing. Results and discussionIn order to an accurate assessment of the proposed method on the mentioned study area, a ground truth image with 11073 pixels has been used as a ground test image. The proposed method has shown an overall accuracy of 92.56 in the study area. The accuracy of detecting changed pixels shows 81.61% and the accuracy of detection unchanged pixels shows 92.77%. The false alarm percentage is 0.21 percent and the missed alarm accuracy is 0.0723 percent. For comparative evaluation, the proposed method is compared with the change vector analysis algorithm. In this section, the selected features in the feature extraction section are entered in the change analysis algorithm, and then the multi thresholding algorithm and shadow analysis used to create the final change map. This method has shown increasing the alarm in comparison with the proposed method. The accuracy of changed and un-changed pixels in the change vector analysis method is equal to 52.98 and 89.24%, respectively. Comparing these results with the results of the proposed method shows the efficiency of the proposed method. ConclusionIn this paper, the new unsupervised change detection method presented based on the combination of multi thresholding and the hierarchical clustering algorithm. Compared to supervised methods that require training data, this method does not require training data. In this method, textural and spatial-spectral features are extracted from images with high spatial resolution, which covers the discussion of the importance of neighborhoods in images with high spatial resolution. In the next step, the extracted features that have a high information content are selected, which helps to reduce the redundancy of the information. The contrast images of the features with high information content are created to differentiate the location of the changes. Spherical computing space is considered as the basic computing space. In order to create a binary change map, two analyzes have been performed on the spherical computational space. First, the Otsu multi-thresholding method has been applied. The values of the smaller and larger thresholds have definite classes. But the value of the middle interval needs to be further analyzed using the hierarchical clustering method. In this section, the middle pixel class is examined, and then a final adjustment is performed using Markov field and shadow and vegetation analysis in order to post-process and prevent false changes. In this paper, the parameters of changed accuracy – unchanged accuracy - overall accuracy - false and missed alarms have been used to evaluate the accuracy of the proposed method with a ground accuracy map. In order to make a comparative study, the proposed method is compared with the change vector analysis method of the created feature space. The results show the efficiency of the proposed method.

Precipitation is the most important hydrological variable that connects the link between atmosphere and surface processes. Three-quarters of the atmosphere heat is the result of releasing the latent heat of evaporation. Therefore, climatic zoning of autumn precipitation is necessary for achieving comprehensive development in different spatial-temporal dimensions. For this purpose, the daily data of autumn season at 18 synoptic stations of the study area have been analyzed with a joint statistical period 32 (1989 - 2018). In this study, Kolmogorov-Smirnov test was used to find out the normality of the data and Mann-Kendall test was used for the process of their changes. Then by applying factor analysis based on principal component analysis and with orthogonal rotation of Varimax it was found that there are two effective precipitation factors in the climate of the region which in total account for more than 68.16% of the variance of the region's autumn precipitation climate. By performing hierarchical cluster analysis and by integrating the matrix of factor scores, two main and sub-regions were identified. These areas are: Areas of high precipitation and moderate precipitation. It can be said that in the high precipitation areas of the territory are located on the rainy of the Zagros, Sahand and Arasbaran mountain belts, stretching as a narrow strip from the north to the south parts of the region.

The research aims to visualize and analyze co-word network and thematic clusters in the field of linked data during 1986-2018.

The study is an applied research in terms of the purpose, which conducted by using co-word analysis as a methodology and descriptive approach. Clusters determined by three methods. VOS Viewer, SPSS, and UCINet were used for data analysis and network visualization.

The keywords linked data and semantic web in terms of  co-word pairs had the highest frequencies. Co-word clustering generated five clusters, while hierarchical clustering produced two clusters. The USA was the most productive country and the highest share of  documents published in various sub-categories of the Computer Sciences. Studies mostly published in health and cultural heritage contexts. The cluster core concepts of the semantic web was the most mature and   central cluster, while linked data usage in the context of cultural heritage was a well-developed but isolated cluster.

Conclustion:

 The results can identify underlying trends and core themes by highlighting thematic gaps to avoid duplicate studies. Policymakers, researchers, and designers of the semantic technologies can plan predictably to develop themes in balance for the future and increase the quality and quantity of scientific outputs.

Equality and justice are important issues in the development process which affect the countries development policies. This paper tries to determine the position and development rank of the Iranian provinces and the development pattern of the country. According to the literature of development indicators, five main indexes were considered for assessing the development of provinces including economy and infrastructure (10 indicators), education (4 indicators), demographic (5 indicators), health (11 indicators), culture, and communication (6 indicators) dimensions. The data were obtained from the National Population and Housing Census 2016 in a cross-sectional format for 31 provinces. In this paper, the ranking of the provinces has been calculated using the TOPSIS method and secondary analysis. Hierarchical clustering used to classify the provinces. The findings show that Semnan, Yazd, and Tehran provinces in comparison with other provinces are at the highest level of development, and the provinces of Sistan and Baluchistan, West Azerbaijan, and Kerman are at the lowest level of development ranking. Besides, the results of this research show that the Core-Periphery model is dominant in the development of the provinces of the country. Moreover, according to the finding of the paper, there is more inequality inside the more developed provinces that indicate the centralization of the facilities in the capital of the provinces.

According to the World Health Organization (World Health Organization, 2014), one eighth of global deaths is because of exposure to air pollution. This is more than twice the previous estimates and confirm that air pollution is currently the greatest threat to the health of the urban environment in the world.Particulate matter is one of the major air pollutants. These particles have sizes ranging from 0.000002 to 500 microns, whereas particles smaller than 2.5 microns have significant health and health effects because they are capable of accessing the respiratory system of the human body.The purpose of this paper is to use statistical methods such as cluster analysis to determine homogeneous time intervals of Particulate matter as the main pollutants of Mashhad. The results can be used by urban planners and decision makers in the discussion of air pollution management, especially planning for the control of air pollution and reducing air pollution.

Concerning the use of statistical methods in air pollution discussion Saksena Joshi and Patil (2002) examined the spatial patterns of air pollution change in New Delhi by cluster analysis method and concluded that there was no statistically significant relationship between pollutant concentration and station type. In another study, Ojeda, Cortina-Januchs, Barrón-Adame, Quintanilla-Domínguez, Hernandez, Vega-Corona, Ruelas and Andina (2010) applied PFCM to clustering algorithm to investigate air pollution in Salamanca, Mexico. Also, air pollution in Bangkok, Thailand, was analyzed using cluster analysis. Using a Geographic Information System (GIS), they mapped air pollution in the area (Prapassornpitaya, Jinsart, & Sanguansintukul 2011).To reduce costs and better management of air quality stations in the Kanto region of Japan, Iizuka, Shirato, Mizukoshi, Noguchi, Yamasaki and Yanagisawa (2014) performed cluster analysis on air pollution data and found that NOX and O3 pollutants can be classified into eight clusters. Each cluster has homogeneity in terms of spatial characteristics and temporal behavior.Ismaili (2018) investigated homogeneous areas of Mashhad air pollution in terms of particulate matter load over a two year period using GIS and cluster analysis method. Results of this study identified three separate homogenous regions for Mashhad.

In the present study, for the temporal analysis of Mashhad air, 10 air quality stations located in the city of Mashhad were used. Hourly data of suspended particles smaller than 2.5 microns (PM 2.5) were collected and arranged in matrices with temporal and spatial scales. Then a hierarchical cluster analysis was performed. Euclidean distance method and Ward method were used to calculate the degree of similarity. The resulting groups were then shown as a dendrogram.

The results of cluster analysis on PM2.5 data of Mashhad showed that three clusters or homogenous groups were recognized. The first cluster is distributed throughout the year. The second cluster with a frequency of 113 days is more mid-year. But the third cluster, which represents the highest concentration of suspended particles, often occurs in the second half of the year, with a frequency of 34 days per year.But on a monthly scale, Shahrivar, Aban and Azar are in the first cluster, and spring months and Mehr in the second cluster. Finally, Tir and Mordad and winter were clustered to the third group.Weekdays are divided into two main groups, with Friday alone in one cluster and other days of the week in the other group. At a lower level, four homogeneous clusters can be identified. Friday and Sunday are in the first and second clusters, respectively, Tuesday and Thursday in the third cluster and in the fourth cluster are Saturday, Monday, and Wednesday.

In this study, particulate matters smaller than 2.5 microns were analyzed using clustering techniques on a scale of days, months, days of a week and hours of a day. The results of hierarchical analysis by Ward's integration method showed that 365 days of the year can be divided into three separate clusters. Also 12 months of the year can be placed in 3 clusters.Months Azar, Aban and Shahrivar are in one cluster, and spring months and Mehr are in the other cluster. Tir and Mordad and months of winter are in the third cluster.Days of the week can be grouped into four categories: Friday and Sunday, respectively, the first and second clusters, and the third cluster, including Tuesdays and Thursdays, and finally, Saturdays, Mondays and Wednesdays. At hourly scale, the results of the study showed that the hours of daylight hours can be divided into homogeneous periods of dawn, morning, evening, and night. The lowest air pollution in the city of Mashhad is in the evening cluster (13-17h). Most of Mashhad air pollution is recorded in the night time, between 18 and 24 hours.

Banks are endeavoring to gain more funds in a highly competitive environment. Given the higher costs of attracting new customers than retaining existing ones, most banks focus on maintaining their existing customers. Therefore, it is quite important for the banks to predict the customer churn in advance. In almost all related research works in banking, customers are divided into two types of static categories: “churners” and “loyal” customers. However, due to the nature of banking particularly in Iran, it is necessary to define churn in a dynamic manner in a variety of circumstances. In this study, the concept of state chain is introduced, which identifies changes in customers’ partial churn status over time. Using the sequence of chains and a combination of hierarchical clustering techniques as well as support vector machine, a model was developed to predict partial churn of bank customers. To construct a practical sample and to evaluate the prediction accuracy, 5 years of real European bank customers’ data as well as 3 years of customers’ data from three different Iranian banks were used. The results indicate a high level of prediction accuracy for the model in all 4 banks, particularly when longer sequences of states are used.

Dendrograms are considered as the most important visualization technique of Hierarchical Clustering. We studied the stability of different dendrograms on the Tehran Stock Exchange and then stabilized the system of TSE indices using Principal Component Analysis (PCA). It seems that the comparison of dendrograms before and after PCA is a quantitative measure of system stability. ( 43% in our study of 21 different indices of TSE which are the representations of the economic sectors of Iranian financial systems. We measured the similarity of different dendrograms according to Baker's Index and the Cophenetic Correlation Index. The results show that stabilized hierarchical clusterings have got better Baker's Index and more reliable dendrogram. Therefore, It is highly recommended to investors, portfolio managers and risk hedgers to denoise and stabilize their clustering with efficient methods like PCA before financial decision making.

All of today's dynamic organizations have selected the Internet as one of the best means of communication with all the individuals and stakeholders. The purpose of this research was to evaluate and cluster the website of the 43 Iranian sports federations based on the Alexa website traffic indicators. The indexes included the number of pages viewed, the average browsing time of the site, the percentage of visitors inside the country, the percentage of visitors from outside the country, the number of links and loading rates. Using hierarchical clustering method, similar federations were grouped together in the form of homogeneous clusters, and eventually three clusters were created. The quality and validity of each of the clusters created by the diagnostic analysis method were confirmed and finally, the characteristics of each of these clusters were analyzed. The basis for cluster analysis was considered to be a comparison of the average of each indicator in each cluster with the average of the same indicator in all the clusters (total data). Since there was no significant difference between the average time of browsing the site and the average number of pages visited among different clusters, these indices were not considered in cluster analysis. The results of the study showed that Iran's sports federations have a poor and unexpected performance in the field of webometrics.

The purpose of this paper is to develop simulation-based healthcare system model in the indigenous interoperability functions in health centers and explain the multilevel relations between variables defined in different levels. Interoperability operational models and various aspects of health levels and accurate explanation of their mechanisms will facilitate relevant administrative processes. In this regard, in this paper an attempt is made to explain the pattern of interoperability in this area. In accordance with the simulation approach, common approaches in this section is extracted and the proposed framework for e-government interaction in the field of health-risk group used. Community groups, experts, managers and experts in the field of health, information technology and management were the population of this study and using cluster-randomized treatment by utilizing sampling pattern and holding meetings with Delphi, factors and dimensions of the proposed model was validated. Finally, the proposed framework and using expert's survey was explained in a way that is closely related to clusters coefficients, optimal interoperability areas in terms of indices, income, time and quality were presented.