List of Articles داده‌کاوی

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Applying data mining techniques to regions segmentation for entrance exams to governmental universities
  نرجس سرعتی آَشتیانی somayyeh alizadeh علی  مبصّـری
  The large numbers of Iranian high school graduates are willing to enter in governmental and popular colleges and compete for it. On the other hand, these graduate students are from various regions with different levels of access to facilities. In opinion of directors of More

  The large numbers of Iranian high school graduates are willing to enter in governmental and popular colleges and compete for it. On the other hand, these graduate students are from various regions with different levels of access to facilities. In opinion of directors of relevant agencies, the quota allocation solves this problem and they are looking to use the knowledge hidden in the data are available in this area.By this way volunteers from each region are compared together and managers are helped to allocate proper quota to related students in regions of each segment. In recent years, quota allocation was determined by Taxonomy that its result is a kind of ranking that does not allow group analyzing and identifies number of region theoretically. To solve this problem clustering is a good strategy. This study is carried out by using data mining techniques and Crisp methods on related dataset from education ministry, interior ministry, ministry of health, and center of statistic and evaluation organization for the first time. After extracting of effective attributes in this area, data preparation, data reduction and combination of attributes using Factor Analysis have done.in next step, by using K-means algorithm, similar items assign in to a cluster that has the minimum distance with centroid mean and then by using neural networks and decision trees, new item can be devoted to each cluster. Finally for assessing created models, accuracy of outputs compared with other methods. Outcomes of this research are: determining the optimal number of sectors, segmenting regions, analyzing each section, extracting decision rules, predicting class labels for new areas faster and more accurately, allowing the appropriate strategies formulation for each section Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Discovering spam in Facebook social network using data mining.
  amin nazari
  In recent years, by developing new technologies and communication facilities such as internet, new aspects named virtual social networks have been created. Rapid development of social networks and huge number of anonymous Users in these networks, created a suitable en More

  In recent years, by developing new technologies and communication facilities such as internet, new aspects named virtual social networks have been created. Rapid development of social networks and huge number of anonymous Users in these networks, created a suitable environment for scammers. Most of the times, scammers are trying to spread several types of spams into these high potential places. Hence, an effective method is required to detect the spams in order to increase the level of information security of people in the social networks. In this paper, a new method for discovering spammer in Facebook social network is proposed. Findings show 99.96% accuracy. In previous papers, users were divided into two groups of ordinary users and spammer users. The method of classification in these papers recognizes also as a spam the users which attached by spammer. So, in this paper by dividing users into three types of ordinary users, spammer and users attached by spammer, accuracy of spam detection has been increased. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - The study of the accuracy of real estate experts' evaluations using a data mining model (Case study of Mellat Bank)
  fatemeh davar
  As the main part of the financial system, banks always face different risks, the most important of which is the credit scoring risk and property valuation. One of the issues faced by property valuation experts is how to evaluate property prices. In general, court expert More

  As the main part of the financial system, banks always face different risks, the most important of which is the credit scoring risk and property valuation. One of the issues faced by property valuation experts is how to evaluate property prices. In general, court experts assess real estate based on price indices. In this research, the researcher aimed to verify the accuracy of valuation experts by using data mining models. This action has been taken to help bank managers and audit reporters to make better decisions about experts and their valuations. Using property valuation indexes and data mining, a predictive model has been developed to predict property prices, and a combination of FCM and K-NN algorithms has been used to achieve a high performance prediction model. This measure was able to greatly increase the predictive accuracy and increase the efficiency of the proposed model. The accuracy level in predicting valuated prices was 84.21% and the RMSE rate in its forecast was 0.43. The proposed approach was tested on real estate valuation data of the Mellat Bank. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  4 - Analyzing the impact of macroeconomic variables on customer churn banking industry With data mining approach
  Mehrnaz Motahari nia
  Today, customer knowledge and understanding of its needs have become a business imperative. Organizations need customer satisfaction to sustain their business and succeed in a competitive market. Knowing customers through customer behavior analysis is possible with the More

  Today, customer knowledge and understanding of its needs have become a business imperative. Organizations need customer satisfaction to sustain their business and succeed in a competitive market. Knowing customers through customer behavior analysis is possible with the use of new technologies such as data mining techniques for organizations. The purpose of this research is to investigate the effective factors on Customers churn in the banking industry. For this purpose, the transaction data of sales terminals of a payment service provider company (PSP) in Iran has been analyzed. In the proposed model using the WRFM method and combining it with the K-Means clustering algorithm, sales terminals are split and loyalty each month. Then, using the additive selection method plus L take R and the multivariate linear regression algorithm, the effective features The percentage of customers discarded is selected from the monthly economic indicators per month. Based on the results of the implementation of the three variables, the index of stock market value, inflation and the price of all coins are the most effective variables among the economic indicators under study. Manuscript profile