خوشه بندی

Open Access Article

1 - Extending the Lifetime of Wireless Sensor Networks Using Fuzzy Clustering Algorithm Based on Trust Model
Farshad Kiyoumarsi Behzad Zamani Dehkordi

Wireless sensor networks (WSNs) are the safest and most widely used existing networks, which are used for monitoring and controlling the environment and obtaining environmental information in order to make appropriate decisions in different environments. One of the very More

Wireless sensor networks (WSNs) are the safest and most widely used existing networks, which are used for monitoring and controlling the environment and obtaining environmental information in order to make appropriate decisions in different environments. One of the very important features of wireless sensor networks is their lifetime. Two important factors come to mind to increase the lifetime of networks: These factors are maintaining the coverage of the network and reducing the energy consumption of sensor nodes simultaneously with the uniform consumption of energy by all of them. Clustering, as the optimal method of data collection, is used to reduce energy consumption and maintain the coverage of the network in wireless sensor networks. In clustered networks, each node transmits acquired data to the cluster head to which it belongs. After a cluster head collects all the data from all member nodes, it transmits the data to the base station (sink). Given that fuzzy logic is a good alternative for complex mathematical systems, in this study, a fuzzy logic-based trust model uses the clustering method in wireless sensor networks. In this way, cluster-head sensors are elected from among sensors with high reliability with the help of fuzzy rules. As a result, the best and most trusted sensors will be selected as the cluster heads. The simulation results in MATLAB software show that in this way, in comparison with K-Means, FCM, subtractive clustering, and multi-objective fuzzy clustering protocols, the energy consumption in clustered nodes will decrease and the network’s lifetime will increase. Manuscript profile

Open Access Article

2 - Presenting a new model for ATM demand scenario
Alireza Agha Gholizadeh Sayyar Mohamadreza Motadel Alireza Pour ebrahimi

In today's competitive world, the ability to recognize predict customer demand is an important issue for the success of organizations. And since ATMs are one of the most important channels for cash distribution and one of the most fundamental criteria for assessing the More

In today's competitive world, the ability to recognize predict customer demand is an important issue for the success of organizations. And since ATMs are one of the most important channels for cash distribution and one of the most fundamental criteria for assessing the level of service to banks,In this paper, the number of referrers to ATM devices is reviewed based on the timing and location of the devices. This article seeks to find a dynamic and functional model for predicting the number of referrers to each ATM depending on the time and location of the device. Hence, 378 ATM machines were used throughout the city of Tehran for a time period of one month, containing 69,418 records. Finally, with the help of clustering of statistical data in spatial and temporal dimensions, this model finally succeeds in learning the pattern in the macro data, and based on the decision tree, the predictor can predict the number of referents to each device, which after the algorithm is presented. In order to improve the quality of banking services and improve the performance of the ATM network, it is proposed to combine the optimal location of ATMs in spatial and temporal dimensions. Manuscript profile

Open Access Article

3 - Presentation of a combined data mining model using associative rules and clustering to identify the dominant patterns of customer behavior (Case study: Ansar Bank)
iman gharib Abbas Toloie Kambiz Heidarzadeh

Background: A new matter that arises in term of the dynamic behavior of customers is considering the customer segmentation. Based on the banks competitions to increase market share as well as the psychological and environmental factors the dynamics of customers’ b More

Background: A new matter that arises in term of the dynamic behavior of customers is considering the customer segmentation. Based on the banks competitions to increase market share as well as the psychological and environmental factors the dynamics of customers’ behavior should be considered over time. Transferring customers to different sectors over time and discovering the dominant models in their displacements between sectors are of important topics in this context. Objective: this article aims to identify the behavioral clusters, the dominant patterns of displacement, and the leading characteristics and patterns of customer displacements with a focus on the customer dynamics behavior of Ansar banks. Design/Methodology: A Hybrid method based on clustering and association rules has been proposed. Finding: four different behavioral group of customer are identified:" low-value customers with sustainable model", "low-value customer with unsustainable profitability model", "turned away customers with average profitability", "loyal customers with low profitability". Relations between of these groups are analyzed by association rules Manuscript profile

Open Access Article

4 - A Comparative Analysis of Mission and Vision Statements of Top Fortune Companies and Iranian Companies in Food Industry using Text Mining and Clustering
Mohsen Shafei nik abadi Ata Karbasi kheyr

Mission and vision statement have an important role in company’s strategic goals and direction, so content analysis of such documents has a positive influence on company’s success. Food industry, as a major means of meeting one of people’s most essenti More

Mission and vision statement have an important role in company’s strategic goals and direction, so content analysis of such documents has a positive influence on company’s success. Food industry, as a major means of meeting one of people’s most essential needs, requires to be improved. Therefore, we aimed to analyze the mission and vision statement of top food industry companies in Iran and the world to understand about similarities and differences in their strategic alignments. The current research is conducted on 53 companies from top 500 fortune list and 42 Iranian pioneer companies based on ministry of industry and mining list. We used text mining technique to process the documents and clustered them into distinct groups by K-Means algorithm. Finally, we ranked the important roots in each cluster using SAW method. Then, we compared the clusters based on their essential components. The findings indicate that while foreign companies focus on customers and image creation, Iranian companies care about products, development and stating general concepts.   Manuscript profile

Open Access Article

5 - Cluster Analysis of Iran's Position in the World and Future Trends Based on Good Governance Components
Mona Ahani Morteza Mosakhani Reza Najafbeigi Mohammad Ali Afsharkazemi

The Study of good governance and the quality of government institutions is a debate that began in the 90's. Good governance, which consists of six components: control of corruption, government effectiveness, political stability and absence of violence, regulatory qualit More

The Study of good governance and the quality of government institutions is a debate that began in the 90's. Good governance, which consists of six components: control of corruption, government effectiveness, political stability and absence of violence, regulatory quality, rule of law, voice and accountability, is a model for development. In this study, the World Bank's assessments and statistics on the six-fold good governance indicators published each year, were used to survey 186 countries worldwide. The aims of this research were studying the status of countries based on good governance and determining the status of Iran among other countries, using clustering technique; And analyzing the trend of Iran's position in the 2021 horizons using time series analysis. Using the clustering method of the countries of the world, based on the good governance and the frequent clustering of Iran with other countries, they were separated. Then, from the time series method using of the exponential smoothing based on the ARIMA's method was investigate to predict the six's good governance indicators and the situation of the country in the next five years. Findings of the research show that in the 2021 horizons, the accountability index will be problematic in the country and the rule of law and control of corruption ratios will remain almost unchanged, and, on the other hand, the rest of the indicators show a slight improvement. Manuscript profile

Open Access Article

6 - Using Clustering and Genetic Algorithm Techniques in Optimizing Decision Trees for Credit Scoring of Bank Customers
Mahmood Alborzi Mohammad Khanbabaei M. E. Mohammad Pourzarandi

Decision trees technique as one of the data mining techniques, is used in credit scoring ofbank customers to classify them in order to offer credit facilities. The main problem is incomplexity of decision trees, excessive size, lack of flexibility and low accuracy incla More

Decision trees technique as one of the data mining techniques, is used in credit scoring ofbank customers to classify them in order to offer credit facilities. The main problem is incomplexity of decision trees, excessive size, lack of flexibility and low accuracy inclassification. The purpose of this paper is to propose a compound model in the optimization ofdecision trees by using genetic algorithm technique. It appears that genetic algorithm can chooseappropriate features and build decision trees to reduce complexity and increase flexibility indecision trees. In the proposed compound model, the credit data is initially divided into twoclusters by Simple means clustering technique. On the next step, the important credit scoringfeatures in the data set are selected using genetic algorithm and the five feature selectionalgorithm based on Filter, Wrapper and Embedded approaches. Subsequently, five decisiontrees based on C4.5 algorithm in each cluster are constructed with a set of the selected features.The best decision trees in each cluster, are selected and combined based on the desiredoptimality criteria, mentioned in this paper, to construct the final decision tree. WEKA machinelearning tool and GATree software were used to in this purpose. Results show that using theproposed compound model in building decision trees leads to increased classification accuracy,compared to other algorithms in this paper. However the algorithm complexity of the proposedcompound model is more than some of the classification algorithms compared in this paper. Manuscript profile

Open Access Article

7 - Using Clustering and Genetic Algorithm Techniques in Optimizing Decision Trees for Credit Scoring of Bank Customers
Mahmood Alborzi Mohammad Khanbabaei M. E. Mohammad Pourzarandi

Decision trees technique as one of the data mining techniques, is used in credit scoring ofbank customers to classify them in order to offer credit facilities. The main problem is incomplexity of decision trees, excessive size, lack of flexibility and low accuracy incla More

Decision trees technique as one of the data mining techniques, is used in credit scoring ofbank customers to classify them in order to offer credit facilities. The main problem is incomplexity of decision trees, excessive size, lack of flexibility and low accuracy inclassification. The purpose of this paper is to propose a compound model in the optimization ofdecision trees by using genetic algorithm technique. It appears that genetic algorithm can chooseappropriate features and build decision trees to reduce complexity and increase flexibility indecision trees. In the proposed compound model, the credit data is initially divided into twoclusters by Simple means clustering technique. On the next step, the important credit scoringfeatures in the data set are selected using genetic algorithm and the five feature selectionalgorithm based on Filter, Wrapper and Embedded approaches. Subsequently, five decisiontrees based on C4.5 algorithm in each cluster are constructed with a set of the selected features.The best decision trees in each cluster, are selected and combined based on the desiredoptimality criteria, mentioned in this paper, to construct the final decision tree. WEKA machinelearning tool and GATree software were used to in this purpose. Results show that using theproposed compound model in building decision trees leads to increased classification accuracy,compared to other algorithms in this paper. However the algorithm complexity of the proposedcompound model is more than some of the classification algorithms compared in this paper. Manuscript profile

Open Access Article

8 - Measuring the efficiency of a three-stage network using data envelopment analysis approach considering dual boundary
Ehsan. Vaeezi S. Esmail. Najafi Seyed Mohammad. Haji Maulana Farhad, Hosseinzadeh Lotfi Mahnaz. Ahadzadeh Namin

This paper presents a method for performance evaluation, ranking and clustering based on the double-frontier view to analyze the complex networks. The model allows us to open the structure of the “black box” and can help to obtain important information about More

This paper presents a method for performance evaluation, ranking and clustering based on the double-frontier view to analyze the complex networks. The model allows us to open the structure of the “black box” and can help to obtain important information about efficient and inefficient points of the system. In this paper, we consider a three-stage network, in respect to the additional desirable and undesirable inputs and outputs and utilize the cooperative approach to measure the efficiency of the overall system. Due to the fact that, a conclusion implying only one of these two, optimistic or pessimistic views is one-sided and incomplete, so, in this paper we used the double-frontier to analyze the network. Moreover, a heuristic technique was used to convert non-linear models into linear models. After obtaining the effective and inefficient points of the network, the DMUs are classified into several clusters by the k-means algorithm.Finally, in this article, in order to apply the proposed model a factory producing dairy products with a production area, warehouse premises and a delivery point are simulated. This factory has been regarded as a dynamic network with a time period of 24 intervals. The results of the ranking showed that, the time periods, (10) and (1) were the best and poorest respectively, in context to the efficiency within 24 phases of time. Manuscript profile

Open Access Article

9 - An Investigation into Factors that Affect Brand Choice Using Factor Analysis Approach
M. Samiei Nasr S. M. Alavi M. Nadjafi Siahroudi

Nowadays, the importance of brand concept is so high that some researchers believe that brand is a perfect product and argue that most of the time customers instead of buying a product, buy the brand. Therefore, recognizing the factors influencing brand choice and inves More

Nowadays, the importance of brand concept is so high that some researchers believe that brand is a perfect product and argue that most of the time customers instead of buying a product, buy the brand. Therefore, recognizing the factors influencing brand choice and investigating the specific features of brand is of importance and requires various studies. The present study aimed at investigating into factors that affect brand choice. The methodology of this study was applied research and for data collection, survey approach was used. The statistical population consisted of buyers in Shiraz and statistical sample based on Factor Analysis were 400 buyers (sampling method: regional method). Based on statistical analyses, 30 factors that affect brand choice were categorized in 9 classifications; five factors, namely the simplicity of brand's pronunciation, non-ambiguity of brand, simplicity of brand memorization; writing language and understandability of brand, were classified as the important ones. Further, based on qualitative analyses, result revealed that “past experience of buying” for respondents affects in brand choice to a great extent. Manuscript profile

Open Access Article

10 - Investigating the Effect of Different Data Clustering Methods on the Accuracy of Models Related to Accounting Estimates by Comparing Traditional and Classical Clustering Methods
S. Mohsen Salehi Vaziri Jamal Barzaghi Khaneghah

Today, the use of accounting information estimation is the same as other disciplines because of the lack of access to all information. For this reason, in this research, we tried to study the accuracy of accounting estimation models using different clustering methods to More

Today, the use of accounting information estimation is the same as other disciplines because of the lack of access to all information. For this reason, in this research, we tried to study the accuracy of accounting estimation models using different clustering methods to determine how different clustering methods increase the accuracy of the desired models and the preferred method Among the different clustering methods, which method can be used to increase the accuracy of the models. The research sample consisted of 99 companies listed in the Tehran Stock Exchange. In order to collect the required data, the financial statements and notes of the 9-year period (2008-2017) were used by the companies. The results of the research showed that the use of different clustering methods increases the accuracy of accounting estimates models in most cases. However, among the clustering methods used in the research, the classic clustering method is a more appropriate method than the method The traditional approach is to increase the accuracy of accounting estimates models. Manuscript profile

Open Access Article

11 - Determination of Homogeneous Hydrological Regions for Estimating Runoff in Ungauged Catchments Using Cluster Analysis (Case Study: Ardabil Province)
Batoul Poorseifollahi Amin Kanooni Mohammadreza Nikpour javad ramezani moghadam

Grouping of catchments based on their climatic factors and physiographic characteristics is a prerequisite for regional analysis of runoff and its use for estimating discharge of catchments without discharge measurement station. In this study, catchments located in Arda More

Grouping of catchments based on their climatic factors and physiographic characteristics is a prerequisite for regional analysis of runoff and its use for estimating discharge of catchments without discharge measurement station. In this study, catchments located in Ardabil province were separated into homogeneous hydrological zones using discharge, mean annual precipitation and physiographic characteristics of catchments and hierarchical clustering method. Due to the large number of parameters, by using principal component analysis, the first four components with 83.6% of total variance were selected as inputs for cluster analysis. Then, the optimal number of clusters was determined by using hierarchical method and drawing the tree diagram, and finally the final clustering was done by K-means method. Subsequently, the sub-catchments that followed a hydrological process were identified using the Dalrymple uniformity test. The results of the uniformity test showed that by excluding stations outside the confidence limits of each cluster, catchments that were similar in terms of annual discharge and other physiographic and meteorological parameters were clustered. Therefore, the number of sub catchments located in clusters 1, 2, 3 and 4 were 8, 4, 9 and 9 catchments, respectively, which can be used in regional analysis to estimate runoff and floods in catchments without discharge measurement data. Manuscript profile

Open Access Article

12 - Clustering of volatility and its asymmetry in Tehran Stock Exchange
زهرا شیرازیان hashem NIKOUMARAM Taghi&acute;&acute;&acute; TORABI

The purpose of this study is to investigate the clustering of fluctuations and its asymmetry in Tehran Stock Exchange. Large changes in prices tend to be large changes and small changes tend to be small changes that are called clustering of fluctuations. On the other ha More

The purpose of this study is to investigate the clustering of fluctuations and its asymmetry in Tehran Stock Exchange. Large changes in prices tend to be large changes and small changes tend to be small changes that are called clustering of fluctuations. On the other hand, higher volatility fluctuations, They tend to form more clusters than small fluctuations, which are referred to as clustering oscillations of oscillations. The volatility of return on assets can directly affect the price of transaction options and the risk of stocks and portfolios. This research is a practical and quantitative research. The statistical society of the time series of the index of Tehran Stock Exchange and the sample used in the time series of return on the total index in the period from the beginning of 2008 to August 2012 is. The index values are extracted from the new rational software and then the logarithmic yield is calculated and analyzed with the Eviews software. Based on the Box and Jenkins approach, the mean ARMA equation was obtained and ARCH test confirmed the existence of clustering fluctuations. The TGARCH model showed asymmetry in volatility and leverage effect. According to the AKIC statistic, the best GARCH model was used for extraction of fluctuations, ETGARCH was introduced. Manuscript profile

Open Access Article

13 - Investigating of effect herding behavior types among analysts on stock price by network analysis in Tehran Stock Exchange
Zahra Shirazian

Herding behavior among security analysts is described as similar behavior by analysts when forecasting main finance ratios of public companies and when giving investment recommendations. This type of behavior can be divided into two categories based on the different dri More

Herding behavior among security analysts is described as similar behavior by analysts when forecasting main finance ratios of public companies and when giving investment recommendations. This type of behavior can be divided into two categories based on the different driving forces behind the analysts’ herding behavior.. For instance, if one analyst lacks the ability to research and provide recommendations, he or she  may follow, or even copy, a famous analyst’s reports. Such  action will result in herding behaviorIn this  paper, we  build  undirected weighted networks to study herding behavior among analysts and  to analyze the characteristics and  the  structure of these networks. We then construct a new  indicator based on the  average degree of nodes and the  average weighted clustering coefficient to research the various types of herding behavior. Our findings suggest  that every industry has, to a certain degree, herding behavior among analysts. Furthermore, we relate the  two  types of herding behavior to stock  price  and find that uninformed herding behavior has a positive effect  on market prices, whereas informed herding behavior has a negative effect.   Manuscript profile

Open Access Article

14 - شناسایی سبک‌های زندگی الکترونیکی جمعیت شهرنشین ایران
حلیم بردی قره جه هرمز مهرانی حسین دیده خانی روح اله سمیعی

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15 - A method for segmenting remote sensing images using the Watershed algorithm and Fuzzy C-Means clustering
Mohsen Hamed Fatemeh Hajiani

In the division of remote sensing image pixels using Watershed segmentation, the boundaries of the image are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that firs More

In the division of remote sensing image pixels using Watershed segmentation, the boundaries of the image are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that first the Watershed algorithm is used to segment the image obtained from the sum of the image derivative with the original image. Image derivation makes the borders of the image well defined and does not overlap between the borders. After segmentation, Fuzzy C-Means clustering is used to combine similar regions. Finally, in order to improve the clustering results, a new segmentation matrix is calculated for each area of the image, according to the characteristics of its neighboring areas. Due to the fact that remote sensing images contain a high level of noise, the proposed algorithm is more capable of dealing with noise compared to the conventional Watershed algorithm, and the edges of the image appear better. The test results of the proposed method on a sample of remote sensing image show the practicality and efficiency of the proposed algorithm. Manuscript profile

Open Access Article

16 - A method for segmenting remote sensing images using the Watershed algorithm and Fuzzy C-Means clustering
Ebrahim Alibabaee Rouhollah Aghajani

In the division of remote sensing image pixels using Watershed segmentation, the image boundaries are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that first the W More

In the division of remote sensing image pixels using Watershed segmentation, the image boundaries are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that first the Watershed algorithm is used to segment the image obtained from the sum of the image derivative with the original image. Image derivation makes the borders of the image well-defined and does not overlap between borders. After segmentation, Fuzzy C-Means clustering is used to combine similar regions. Finally, in order to improve the clustering results, a new segmentation matrix is calculated for each area of the image, according to the characteristics of its neighboring areas. Due to the fact that remote sensing images contain a high level of noise, the proposed algorithm is more capable of dealing with noise compared to the conventional Watershed algorithm, and the edges of the image appear better. The test results of the proposed method on a sample of remote sensing image show the practicality and efficiency of the proposed algorithm. Manuscript profile

Open Access Article

17 - Evaluation of soil salinity by analyzing Landsat-8 images and field Observations (Case study: Behesht-e- Gomshodeh, Fars province)
Mohamad Kazemi Fariborz Mohammadi Ali Reza Nafarzadegan

Soil salinity is considered as one of the potential environmental hazards. The purpose of this study was to find the best index and the most suitable relationship for estimating soil salinity and its mapping using remote sensing data. At the first step, random sampling More

Soil salinity is considered as one of the potential environmental hazards. The purpose of this study was to find the best index and the most suitable relationship for estimating soil salinity and its mapping using remote sensing data. At the first step, random sampling was performed using fishnet method and surface soil electrical conductivity (EC) measurements. Then, the threshold levels (92%, 95%, and 98%) were applied to the output images of each indicator. The methodology included using the least squares fitting (LS-fit) technique and principal components analysis (PCA) for halite and gypsum minerals, determining the correlation between the output of indices and ground data, and performing clustering and factor analysis between EC and output images. In order to select the best model derived from Landsat-8 band combinations and the amount of salinity, collinearity test, Durbin-Watson test, and backward multivariate regression were employed. The Cohen‘s kappa coefficient was also applied to evaluate the multivariate regression formed by Landsat-8 bands. The performance of the indicators was evaluated based on four criteria of root mean square error (RMSE), mean bias error (MBE), mean absolute error (MAE) and R-squared (R2). The results of the factor analysis showed the smallest distance between the EC, salinity index (SI) and brightness index (BI). The SI with an amount of 0.89 had the highest Pearson correlation with EC. In the dendrogram diagram, SI index with EC was placed in a cluster, and the RMSE, MBE, MAE and R2 values of the SI index were estimated to be 0.16, 0.11, 0.12, and 0.76, respectively. Compared to the rest of the indicators and linear, multivariate regression (with Cohen‘s kappa coefficient of 60%,), the SI index has provided better outcomes. Manuscript profile

Open Access Article

18 - Use data mining to identify factors affecting students' academic failure
Mahmood Najafi Mehdi Afzali Mahmood Moradi

Knowledge extraction is one of the most significant problems of data mining. The principles raised in if-then format can be turned into real numbers in each section- as values which could be included in dataset. The suggested method in the present dissertation is applic More

Knowledge extraction is one of the most significant problems of data mining. The principles raised in if-then format can be turned into real numbers in each section- as values which could be included in dataset. The suggested method in the present dissertation is application of decision tree algorithms, clustering and forum rules for extraction of final rules. In the suggested method, extraction of rules is defined as an optimization problem and objective was obtaining a rule of high confidence, generalization and understandability. The suggested algorithm for extraction of rules was obtained from and tested based on a dataset of educational failure of 256 art school students living in Zanjan. The results suggested that the j48 algorithm in decision tree and accuracy of 0.95 is the choice for the dataset of educational failure. Data clustering was done by K-Main algorithm with confidence coefficient of 0.95. After all, obtaining rules of high confidence coefficient was done based on forum rules from Apriori algorithm for the whole datasets. The results of present study could be used for inhibition of educational failure of students, improved quality of relationship of parents and authorities with students and enhancing the education they receive. Manuscript profile

Open Access Article

19 - A new algorithm for data clustering using combination of genetic and Fireflies algorithms
Mahsa Afsardeir mansoure Afsardeir

Introduction: With the progress of technology and increasing the volume of data in databases, the demand for fast and accurate discovery and extraction of databases has increased. Clustering is one of the data mining approaches that is proposed to analyze and interpret More

Introduction: With the progress of technology and increasing the volume of data in databases, the demand for fast and accurate discovery and extraction of databases has increased. Clustering is one of the data mining approaches that is proposed to analyze and interpret data by exploring the structures using similarities or differences. One of the most widely used clustering methods is the k-means. In this algorithm, cluster centers are randomly selected and each object is assigned to a cluster that has maximum similarity to the center of that cluster. Therefore, this algorithm is not suitable for outlier data since this data easily changes centers and may produce undesirable results. Therefore, by using optimization methods to find the best cluster centers, the performance of this algorithm can be significantly improved. The idea of combining firefly and genetics algorithms to optimize clustering accuracy is an innovation that has not been used before.Method: In order to optimize k-means clustering, in this paper, the combined method of genetic algorithm and firefly worm is introduced as the firefly genetic algorithm.Findings: The proposed algorithm is evaluated using three well-known datasets, namely, Breast Cancer, Iris, and Glass. It is clear from the results that the proposed algorithm provides better results in all three datasets. The results confirm that the distance between clusters is much less than the compared approaches.Discussion and Conclusion: The most important issue in clustering is to correctly determine the cluster centers. There are a variety of methods and algorithms that performs clustering with different performance. In this paper, based on firefly metaheuristic algorithms and genetic algorithms a new method has been proposed for data clustering. Our main focus in this study was on two determining factors, namely the distance within the data cluster (distance of each data to the center of the cluster) and the distance that the headers have from each other (maximum distance between the centers of the clusters). In the k-means algorithm, clustering is not accurate since the cluster centers are selected randomly. Employing firefly algorithms and genetics, we try to obtain more accurate centers of the clusters and, as a result, correct clustering. Manuscript profile

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20 - طراحی و ارائه رویکرد ترکیبی نوین در جهت انتخاب و پیشنهاد مکانی و زمانی بر پایه شبکه عصبی کانولوشن
صدف صفوی مهرداد جلالی محبوبه هوشمند

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21 - تشخیص سرطان خون نوع لوسمی حاد ALL در تصاویر لام خون با استفاده از روش ترکیبی خوشه بندی فازی و شبکه عصبی
محمدمهدی حسینی زهرا سهرابی

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22 - Temporal Graph Partioning for Clustering in Tagging Systems
Ali Akbar Alah Daghi Mehdrad Jalali Seyyed Javad Seyed Mahdavi Chabok

Today, information growth in the world of Web 2.0, due to the vast amount of data and change of some concepts over time, there is a lot of unnecessary and irrelevant information to what users are looking for. In this paper, we for solve this problem, propose temporal cl More

Today, information growth in the world of Web 2.0, due to the vast amount of data and change of some concepts over time, there is a lot of unnecessary and irrelevant information to what users are looking for. In this paper, we for solve this problem, propose temporal clustering of tags for systems that use tags as a metadata and are changing over time. The way we use for clustering, is temporal graph partitioning tags by changing the tag similarity weights during the time, then clustering will change and adapt itself with the changes. To demonstrate the effectiveness of this approach, we implemented it on a data set of MetaFilter site and compared it with similar methods. The results show that our proposed methods improved F-Measure out 24% compared to best clusters in the same way, over time, has improved and its concept is associated both with the past concepts and the newsletter. Manuscript profile

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23 - کاهش بعد داده ها ضمن حفظ خوشه های داده
محبوبه حقایقی پور یحیی فرقانی محمدحسین معطر

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24 - ارزیابی شاخص های شهر سالم با استفاده از مدل تاپسیس فازی، نمونه موردی: (مناطق ده گانه شهر شیراز)
محمدرضا سلیمی سبحان بابر منصوری

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25 - Presenting a model for statistical process control in order to optimize efficiency and quality in manufacturing industries
abbas morovvati Seyed Jalaledin Hosseini Ghoncheh Hasan Haleh

10.30495/imj.2023.1988808.1854

In this research, a combined statistical process control model is presented to identify factors affecting efficiency and quality in manufacturing and component manufacturing industries, and then controlling and optimizing these processes is considered. Manufacturing and More

In this research, a combined statistical process control model is presented to identify factors affecting efficiency and quality in manufacturing and component manufacturing industries, and then controlling and optimizing these processes is considered. Manufacturing and component industries are considered as the main body of the country's industries for case study and implementation. Clustering techniques are used to discover factors affecting efficiency. And then using decision tree algorithms to predict efficiency and quality in these industries, and in the final stage, control charts of dispersion and average variables are used to draw control charts. The comparison table of the parameters is prepared by the output of the Clementine software, and RapidMiner software is used in the neural network section. The results obtained from the identification of influencing and forecasting factors are close to the target values from a technical point of view, and the control charts are consistent with the technical control limits of the characteristics and are useful for optimizing the target value, which is efficiency and quality. Manuscript profile

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26 - The Statistical Examination of Ionic Ratio and Saturation Indexes to Investigate the Origion of Underground Water Resource Salts from Delfan Plain at North-Lorestan
Tayebeh Karkhaneh Ramin Sarikhani Artims Dehnavi

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27 - The Statistical Examination of Ionic Ratio and Saturation Indexes to Investigate the Origion of Underground Water Resource Salts from Delfan
Tayebeh Karkhaneh ramin sarikhani Artims Ghasemi

This research aims to examine underground water of delfan city in terms of geochemical characteristicsTo this end,the main elements of underground water were analyzed.Based on which all parameters were lower than allowed limit.As the saturation index can be an important More

This research aims to examine underground water of delfan city in terms of geochemical characteristicsTo this end,the main elements of underground water were analyzed.Based on which all parameters were lower than allowed limit.As the saturation index can be an important factor to understand solvation –setllement of mineral available in underground water ,the saturation index was calculated using computerized code phreeqc.The saturation index of the studied minerals in all water specimens was negative and all considered minerals can be solving.Also ,based on ionic exchange diagrams,sodium and cholor have two different origins and Calcite,Dolomit and Gypsum  solvations have occurred from which Calcite and Dolomit solvations were higher.According to the HCA,samples are in two main clusters which Anions-Kations concentration in one cluster samples were higher .According to the clusters stiff diagram the region water type is Bicarbonate –Casic .To find main factor of underground water chemistry ,rotational Varimax method has been used which is the most common PCA,because it gives more interpretable elements.By this method,the limestones and Dolomit,s solvation and rock-water interaction are the most important factors of the region,s underground water chemistry Manuscript profile

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28 - اعتبار سنجی مشتریان بانک با استفاده از خوشه بندی به روشK-Means123
محمدرضا مهرگان رضا تهرانی عبداﷲ نظری

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29 - درک الگوی تحرک و فعالیت های کاربر از داده های شبکه های اجتماعی با برچسب جغرافیایی
حسن رحیمی نرگس گلستانی رضا گلستانی

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30 - A New Clustering Algorithm for Productivity in Data Mining: The Case of UCA Data
Jhila Nasiri Farzin Modarres Khiyabani NIma Azorbaarmir Shotorbani

10.30495/qjopm.2020.1867405.2443

Methods of clustering in data mining have dramatically developed in recent years as a result of the crucial need to categorize data leading to the expansion of data mining techniques and enhanced productivity of clustering methods in management and decision making. Whal More

Methods of clustering in data mining have dramatically developed in recent years as a result of the crucial need to categorize data leading to the expansion of data mining techniques and enhanced productivity of clustering methods in management and decision making. Whale optimization algorithm is a new stochastic global optimization method employed to resolve various problems. We already presented a data clustering method based on Whale optimization algorithm in which the initial solutions are randomly selected. What has made K-mean algorithm a highly popular clustering approaches appealing to many researchers is the simplicity and brevity of the stages involved in the process. The present enquiry aimed at employing K-mean algorithm to improve the capability of Whale optimization clustering and proposing the hybrid KWOA algorithm which can find more accurate clusters. The computational results of running the newly proposed algorithm, along with some well-known clustering algorithms, on real data sets from a well-known machine learning repository underscored the promising performance of the proposed algorithm in terms of the quality and standard deviation of the final solutions.  Manuscript profile

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31 - Studying the effect of evapotranspiration on the temperature near the surface of the earth using the LCZ algorithm in Tehran and Alborz provinces.
Ali teymoori Saeed Jahanbakhsh Ali mohammad Khorshid Dost

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 More

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. Manuscript profile

Open Access Article

32 - Assessment of Different Methods of The Estimation of Reference Evapotranspiration By FAO’s Evaporation Pan Method in Catchment Basin of East And South Eastern of The Country
Javad Khoshhal Hamid Zare Abyaneh Alireza Joshani

Assessment of reference evapotranspiration to estimate aqueous Plants’ needs, to manage aquatic and drainage plans and irrigation timing of plants are some necessities of agriculture section. So in this research to find the best model for estimation of refere More

Assessment of reference evapotranspiration to estimate aqueous Plants’ needs, to manage aquatic and drainage plans and irrigation timing of plants are some necessities of agriculture section. So in this research to find the best model for estimation of reference evaporation and transpiration for catchment basin of East and South Eastern of the Country, relying on clustering method and considering data made by 66 weather stations, reference evaporation and transpiration made by evaporation basin was assessed and compared to evaporation and transpiration measures of 8 equations based on statistical parameters: r, t, d, MAE, MBE, NRMSE. Based on the results, considering the different time dimensions, the  methods Hargreaves - Samani, Blaney – Criddle – FAO 24, Turk and Priestley – Taylor, have the best conformity with reference evapotranspiration values resulted by evaporation pan. Also the results of research show that in seasonal scale, summer had the minimum and winter had the maximum estimation error of ETO .On the other hand, monthly scale had the minimum error compared to seasonal scale. This result show decrease in ETO error in small time scales Manuscript profile

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33 - Synoptic Analysis of Dust From The Warm Half of The Year in Southern Khorasan Province
Zohra Ahmadi Reza Doostan Abbas Mofidi

Dust is the first Natural hazard in desert and semi-desert world and Iran. In order to identify the days of the dust of South KHorasan, the daily amount of horizontal visibility, wind speed and direction in the spring and summer of 1991 -2008 were received from Meteorol More

Dust is the first Natural hazard in desert and semi-desert world and Iran. In order to identify the days of the dust of South KHorasan, the daily amount of horizontal visibility, wind speed and direction in the spring and summer of 1991 -2008 were received from Meteorological Organization. Then, based on Shao and Dong index, the days were extracted with dust. In order to determine atmospheric pattern led to the dust, the daily Geopotential height 500 HP from the National Center for Environmental Prediction America/ National Center for Atmospheric Research (NCEP / NCAR), was prepared. In this study, to determine the pressure patterns were used the principle component analysis approach in the state S and hierarchical clustering (ward). Then the composite maps of vorticity, geopotential height, and sea pressure and flow pattern for each pattern produced and were analyzed. The results showed that the tow dominant synoptic patterns respectively are the summer pattern with a subtropical high on Iran and high low on Pakistan in the high levels of the atmosphere and the pressure difference between the South-East of Iran (low pressure) and the Caspian Sea (high pressure) on the land surface. In this pattern, the wind from the East of the Caspian Sea and Turkmenistan desert in the dry lands and deserts to eastern Iran flows and causing dust. This atmospheric conditions is this Connection with the 120-day winds of Sistan and Baluchestan in Iran's East. In the spring pattern, the trough of westerly winds in the center and east of Iran in the high level of atmosphere and low pressure on the ground in this area has led to unstable weather conditions in the central deserts of Iran and the West the province that these flows transfer dust particles and reduce visibility. As well as intense pressure difference between the centers lead to dust storms in the South Khorasan province Manuscript profile

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34 - الگوهای جوی تداوم بارش های غرب ایران
شیدا منصوری رضا دوستان

الگوهای جوی تداوم بارش های غرب ایرانبر مبنای 268 تداوم دو روزه و 162 تداوم سه روزه و بیشتر باروش تحلیل مولفه اصلی و خوشه بندیباداده ارتفاع ژئوپتانسیل متر تراز 500 هکتوپاسکال از مرکز ملی پیش بینی محیطی و تحقیقات جویدر دوره 1961-2010 تعیین گردید.چنانکهدر تداوم دوروزه بارش More

الگوهای جوی تداوم بارش های غرب ایرانبر مبنای 268 تداوم دو روزه و 162 تداوم سه روزه و بیشتر باروش تحلیل مولفه اصلی و خوشه بندیباداده ارتفاع ژئوپتانسیل متر تراز 500 هکتوپاسکال از مرکز ملی پیش بینی محیطی و تحقیقات جویدر دوره 1961-2010 تعیین گردید.چنانکهدر تداوم دوروزه بارش، حرکت نصف النهاری بادهای غربی در خاورمیانه جدای از آرایش مداری بادهای غربی درعرض میانی غالب است.،اما آرایش نصف النهاری یکدست بادهای غربی از عرض بالا تا دریای سرخ، وقوع سردچال آناتولی و عمیق تر شدن فرود شرق مدیترانه در سطوح میانی جو، همراه با ترکیب دو پرفشار سیبری و جنوب اروپا تا شمال آفریقا در دو طرف کم فشارهای مدیترانه شرقی و سودان در سطح زمین، شکل گیری سیکلون های قوی تر و تداوم بیشتر بارش های غرب ایرانرا موجب میگردند.چنانکه در الگوهای جوی،جریانات مرطوب با جهت جنوب و جنوب غربی با موقعیت واچرخند غالب در عمان و چرخند های مدیترانه شرقیو غرب ایران، با گذر از جلگه عراق و خوزستان بر ارتفاعات بلند زاگرس صعود و بیشترین ریزش ها مداوم را در غرب ایران به همراه دارند. Manuscript profile

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35 - Impact of Sink Node Placement onto Wireless Sensor Networks Performance Regarding Clustering Routing and Compressive Sensing Theory
Shima Pakdaman Tirani Avid Avokh

Wireless Sensor Networks (WSNs) consist of several sensor nodes with sensing, computation, and wireless communication capabilities. The energy constraint is one of the most important issues in these networks. Thus, the data-gathering process should be carefully designed More

Wireless Sensor Networks (WSNs) consist of several sensor nodes with sensing, computation, and wireless communication capabilities. The energy constraint is one of the most important issues in these networks. Thus, the data-gathering process should be carefully designed to conserve the energy. In this situation, a load-balancing strategy can enhance the resources utilization, and consequently, increase the network lifetime. Furthermore, recently, the sparse nature of data in WSNs has been motivated the use of the compressive sensing as an efficient data gathering technique. Using the compressive sensing theory significantly leads to decreasing the volume of the transmitted data. Taking the above challenges into account, the main goal of this paper is to jointly consider the compressive sensing method and the load-balancing in WSNs. In this regards, using the conventional network model, we analyze the network performance in several different states. These states challange the sink location in term of the number of transmissions. Numerical results demonstrate the efficiency of the load-balancing in the network performance. Manuscript profile

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36 - شناسایی سبک‌های زندگی الکترونیکی جمعیت شهرنشین ایران
حلیم بردی قره جه هرمز مهرانی حسین دیده خانی روح اله سمیعی

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37 - بهبود خوشه بندی خودکار با بکارگیری الگوریتمهای فراابتکاری چند هدفه با ارائه معیار ارزیابی جدید و کاربرد آن در ریسک اعتباری
مجید محمدی راد مهدی افضلی

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38 - Automakers Clustering based on Economic Production Function using Data Envelopment Analysis
S. Rezaei Gh.R. Amin M.Gh. Ariyanezhad

DEA-based clustering approach reveals the input–output relationships hidden in the data items of input and output. DEA-based clustering approach employs the piece-wise production functions derived from the DEA method to cluster the car companies. Estimate produ More

DEA-based clustering approach reveals the input–output relationships hidden in the data items of input and output. DEA-based clustering approach employs the piece-wise production functions derived from the DEA method to cluster the car companies. Estimate production function for each car company by input-output data is the benefit of this method. Thus, each car company (like Saipa) not only knows the cluster that it belongs to, but also checks the production function type that it confronts. It is important for managerial decision-making in different fields where decision-makers are interested in knowing the changes required in combining input resources. Manuscript profile

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39 - رویکرد داده‌کاوی در بخش‌بندی بازار مشتریان به منظور اتخاذ استراتژی‌های کارا (مطالعه موردی صنعت مخابرات)
محمد ولایتی فرهاد حسین زاده لطفی محمدرضا شهریاری فریدون رهنمای رود پشتی

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40 - Presenting a customer classification Pattern with a combined data mining approach (case study :Hygienic and Cosmetic products Industry )
omid Bashardoust Ezzatollah Asgharizadeh moHammadAli AfsharKazemi

Due to the accumulated volume of customer purchasing information and the complexity of competition in the present era, the importance of creating a platform for analyzing up-to-date and accurate customer data, with the aim of creating effective relationships with curren More

Due to the accumulated volume of customer purchasing information and the complexity of competition in the present era, the importance of creating a platform for analyzing up-to-date and accurate customer data, with the aim of creating effective relationships with current and loyal customers, more than ever for organizations as It has become a competitive advantage. The purpose of this study was to investigate the behavioral patterns of customers buying Hygienic Products in order to classify them based on the WRFM using data mining methods. 65534 samples were collected from the company databases in the period of 1396-1397 among the customers of Tehran province by the available purposeful sampling method, also with the help of SPSS, the amount of WRFM determined according to the opinion of industry experts and then this field had been to other fields in the research and using Clementine software, customers clustering has been done according to 70% of the data; also, in order to evaluate the quality of clustering, the criteria of Gini Score, error percentage, and normalized mutual information were used. The results indicate the high efficiency of the K-Means clustering method with the number of four clusters with purity percentage (0.761) for customer segmentation. Manuscript profile

Open Access Article

41 - Analysis of Financial networks in Tehran Stock Exchange using the application of centrality measures
Majid Montasheri Hojjatollah Sadeqi

The purpose of this study is to create a Financial network to identify stock market leaders using centrality measures.This study finally provides a clustering of superior stocks that can be used as an optimal stock portfolio by investors.The statistical population of al More

The purpose of this study is to create a Financial network to identify stock market leaders using centrality measures.This study finally provides a clustering of superior stocks that can be used as an optimal stock portfolio by investors.The statistical population of all stock exchanges is that the 100 companies with the most capital were selected as a statistical sample over a period of 11 years.Due to the nature of the research ranking, the Kendall correlation coefficient was used to calculate the correlation.The Prime algorithm was used to identify relationships and construct the minimum spanning tree, and the fast-greedy algorithm was used to cluster stocks.The results show that in terms of degree centrality measure, stocks of Sepahan Cement companies,Omid Investment Management and Bank Melli Investment, based on closeness centrality measure, stocks of Sepahan Cement companies,International Building Development, and Khuzestan Steel, based on Betweenness centrality measure, the stocks of Sepahan Cement, Ghadir Investment and Bank Melli Investments, and finally based on the bottleneck centrality measure,the shares of Sepahan Cement, Khuzestan Steel and International Building Development have the greatest impact on the stock exchange network. Also, the top stocks were classified into 11 clusters,each of which shows a strong relationship between its components. Manuscript profile

Open Access Article

42 - Online Portfolio Selection Using Spectral Pattern Matching
Matin Abdi amirabbas najafi

Nowadays, due to the rise of turnover and pace of trading in financial markets, accelerating of analysis and making decision is unavoidable. Humans are unable to analyze big data quickly without behavioral biases. Hence, financial markets tend to apply algorithmic tradi More

Nowadays, due to the rise of turnover and pace of trading in financial markets, accelerating of analysis and making decision is unavoidable. Humans are unable to analyze big data quickly without behavioral biases. Hence, financial markets tend to apply algorithmic trading in which some techniques like data mining and machine learning are notable. Online Portfolio Selection (OLPS) is one of the most modern techniques in algorithmic trading. OLPS allocates capital to a number of stocks and updates portfolio at the beginning of each period by some techniques. Actually, individual has no role in portfolio selection and the algorithm determines the way of investing in each period. In this article, an algorithm which follows pattern matching principle has been introduced. In pattern matching principle, the portfolio is selected based on identical historical patterns and in this article these patterns are found by spectral clustering in data mining. At the end of article, there is a numerical example which uses the most 20 active stocks in New York Stock Exchange (NYSE) data and its results has been compared with other algorithms in this topic. Manuscript profile

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43 - Taxonomy of Promotion Strategies of the Prosperous Pharmaceutical Products in the Growth Stage
Mahdi Ebrahimi ali asgarhalvaei

Iran's pharmaceutical industry has long been confronted with various marketing and advertising constraints Most of these constraints has arised from governmental terms and conditions, and has led to the overwhelming majority of these companies pursuing a passive and min More

Iran's pharmaceutical industry has long been confronted with various marketing and advertising constraints Most of these constraints has arised from governmental terms and conditions, and has led to the overwhelming majority of these companies pursuing a passive and minimalistic approach to exploiting promotional strategies. However,today, with the arrival of newly stablished pharmaceutical companies and with private sector support, we are observing a remarkable change in the past approaches of these companies toward their promotion strategies.  In this research, presenting the latest findings of promotion strategies of human pharmaceutical companies, we aimed to identify the common types of these strategies using the taxonomic method. For this purpose, we first created a comprehensive framework for the dimensions and components of the promotion strategy of pharmaceutical companies by conducting semi-structured interviews in a qualitative research and using a content analysis method ,then, through a quantitative survey and completion of questionnaires, the promotion strategy of each of the forty pharmaceutical companies in the statistical sample of this study was identified. Finally, by performing fuzzy clustering, four clusters or distinct types of promotion strategies of pharmaceutical companies were identified, each with significant differences in some of the key characteristics of other types. A key result of study shows that pharmaceutical companies have adopted different approaches to using promotion strategies. Manuscript profile

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