clustering algorithm

The purpose of this article is to present a model of academic guidance for first secondary students based on cumulative hierarchical clustering algorithms. Our goal in this article was to simultaneously consider the basic courses of each field along with the skills needed by students to succeed in studying in different fields.This research is of applied type. The statistical population of the research is 30 students of Zhanbazan high school in Sirvan city of Ilam province. Data is collected through the scores of students' report cards and the scores assigned by the school counselor in the field of skills required for each field of study.The main algorithm of the research is the single link algorithm and modeling, data analysis has been done using spss software. By clustering all the data and research variables, first 10 clusters and finally 3 main clusters containing the most similar research samples were formed. . Examining and comparing the obtained clusters with the actual results of students' academic guidance showed a high degree of similarity. Comparing the results of clustering by the algorithm and the actual results of students' choice of fields showed 100% agreement in the field of experimental sciences, 77% in the field of experimental sciences, and 85% in the field of humanities.

The use of traditional forecasting tools and methods has a high error and has a poorer performance compared to newer methods and nonlinear models. One of the most widely used methods and algorithms in predicting the use of machine learning. The main purpose of this study is to investigate the application of machine learning in providing a model for predicting the bankruptcy of 308 companies listed on the Tehran Stock Exchange in the period 1389 to 1398 (3080 years - company) to test the hypotheses of multiple regression of composite data. In order to implement the Medians-K clustering algorithm and related calculations, R statistical calculation software was used. The results show that among the financial ratios identified in the first model, only the ratio of net income to total assets and the ratio of market value of equity to total market value can improve the ability of the Altman bankruptcy prediction model. Also, in the second model, the specified financial ratios have the ability to improve the bankruptcy forecast model, and by adding the Devscore variable for groups based on industry and size, the modified model improves the bankruptcy forecast, The results shows that a company is more likely to go bankrupt if it has bankruptcy-related financial ratios that are lower than the average of its cluster peers..

Abstract Machine learning is a broad discipline that has designed learning algorithms that can guide stimuli, detect spoken language, and discover hidden adjustments in data volume growth, of which financial data is no exception. Therefore, the purpose of this study is to investigate the application of machine learning in providing a model for detecting accounting distortions in companies listed on the Tehran Stock Exchange. Statistical analysis of the research was performed based on the extracted data of 308 companies listed on the Tehran Stock Exchange in the period 1389 to 1398 (3080 years - company) and the screening method was used for sampling. Accounting distortions of the dependent variable obtained through the virtual variable zero and one and the variables of non-discretionary accruals, change in accounts receivable, change in inventory, soft assets, change in cash sales, change in return on assets and issuance of securities as Independent variables are considered and nonlinear regression is used to test the hypotheses and R statistical calculation software is used to implement the Medians-K clustering algorithm and related calculations.

Assessing the feasibility of optimum currency area in OIC countries: using fuzzy and hard clustering algorithmAbstractThe Organization of Islamic Cooperation (OIC) was established in 1971 and includes 57 Islamic countries. The most important goal of this organization is strengthening and expanding political, economic, and commercial ties and creating a common market among its member countries. Considering the successful experience of Euro area, it might be possible that a common currency area could help the OIC countries to achieve the integration. Therefore, the purpose of this study is to investigate the possibility of forming a common currency area among the OIC countries. Based on the convergence criteria presented in the optimum currency area literature, the study uses two different clustering algorithms, i.e. fuzzy and hard clustering methods to cluster the OIC countries. The results show that there are adequate similarities in the economic structures of the Arab Economic Union (AEU) countries. Therefore it seems plausible to create a common currency area among the AEU countries. In addition, a subgroup of the Economic Cooperation Organization (ECO) countries also can join this common monetary union.

Urban population growth and increasing demand for housing and services in the cities of the country has forced the need for physical development of cities this development in recent decades has been more of external development, and the provision of services and housing has been following by adding to the city's area, that has caused to urban sprawl and deterioration of surroundings and neighboring gardens and agricultures. While that infill development and particularly redevelopment and utilization of land as effective approaches can be considered in reducing damage to the environment and realization of sustainable development of cities. In this regard the aim of this research is to develop a model to identify lands with redevelopment capability and use it to the city of Arak as an example. Therefore, this research tries to answer these questions that how to identify the lands with redevelopment capability? And what are these areas in Arak? For this purpose, an innovative algorithm to identify lands with destruction and redevelopment capability is designed, that urban parts are evaluated on the basis of 8 criteria, construction quality, age of Construction, small lot, permeability, land's price, sponsorship rate, ratio of migration and access to green space. This innovative algorithm has been implemented for example in the city of Arak. The results show that innovative algorithm has nice capability in combination with GIS software and can be used to identify the lands with destruction and redevelopment capability. Also using this algorithm in Arak city lead to identify 4187 pieces with redevelopment capability which has an area of 85.8 hectares and can be used to realization of infill development in Arak city.

The quality of development and its infrastructures have brought about major problems to the development process of the country’s villages due to unfavorable and concentrated planning. Its various dimensions and structural complexity forms a basic limitation to the presentation of an appropriate model for the distribution of budgets. Therefore، awareness of distribution of development in rural regions is essential for planning and presenting rural development programs. This research aims at determine levels of development in the Central district of Boyer-Ahmad county. The recent study is a functional one due to the aim، and it is a quantity study، due to data analysis. Statistical population of the study was the villages in the county with 20 or more households (89 villages). The sample size of the rural people was determined using the sampling size table given by Lin. The final sample included 566 rural households were randomly selected as a sample of the study from the population. To collect the data، a questionnaire was used. The Cronbach''s alpha coefficient (0. 561-0. 955) indicated the optimality of the questionnaire. In order to zoning development of rural areas، organism approach (structured) to select criteria was used. In order to determine the zoning rural development، descriptive analysis and K-means algorithm in combination with imperialist competitive algorithm (ICA) were used. Data processing was performed using MATLAB 7. 10 software. Findings of study indicate the answer of optimum of K-MICA algorithm is better than k-means algorithm. Also the worst answer of K-MICA algorithm is better than the best answer of k-means algorithm. The findings also indicated the undesirable rural development in the region. Also، approximately half of villages with 49. 43 percent of the rural region were undeveloped villages. In developed level، 28. 08 percent of villages and in developing level، 22. 47 percent of villages.