Sharif Digital Repository

One of the important tasks of the customer relationship management of any company is to control the customer churn rate. Because according to research, retaining existing customers for a company costs less than attracting new customers. One of the most important tasks in order to control customer churn is to anticipate customers who intend to leave the company. This thesis proposes new method for customer churn prediction in mobile telecommunication industry. Mobile operators are structured in such a way that they can record and store a lot of data. In this dissertation, the most appropriate operator data was selected using data mining methods and various features related to customer churn... 

Ⅾue to the absenⅽe of the ⅼabeⅼs or so−ⅽaⅼⅼeⅾ target variabⅼe، ⅽⅼustering vaⅼiⅾation، ⅾespite ⅽⅼassifiⅽation, is not that straightforward. So، the ⅽⅼuster evaⅼuation is a ⅽhaⅼⅼenging task both in researⅽh projeⅽts anⅾ appⅼiⅽations. Whiⅼe ⅿany ⅽⅼustering vaⅼiⅾity inⅾiⅽes are aⅾⅾresseⅾ in the ⅼiterature, ⅿost of theⅿ, even those wiⅾeⅼy useⅾ in the appⅼiⅽation, ⅽannot hanⅾⅼe arbitrary shapes. In this paper, a noveⅼ ⅽⅼustering vaⅼiⅾity inⅾex is proposeⅾ, whiⅽh is ⅿuⅽh ⅿore powerfuⅼ in ⅽapturing the ⅾata’s reaⅼ struⅽture anⅾ ⅾeaⅼing with arbitrary shapes. An aⅼⅿost noveⅼ separation ⅿeasure is proposeⅾ to represent the signifiⅽant or insignifiⅽant separation regarⅾing the ⅽⅼuster’s struⅽture... 

Insurance companies in Iran operate in a completely competitive and dynamic environment. Because customer acquisition in these companies is significantly more expensive than customer retention, with timely forecasting of churning customers, they can manage their customers more effectively. In this study, in order to predict customer churn in the insurance industry, the data of one of the Iranian insurance companies that has more than two million insurers were used. In order to identify important data and variables, previous studies were reviewed, and on the other hand, the Central Insurance Regulations of the Islamic Republic of Iran, as well as the information of the insurance contracts of... 

Nowadays, investigating the factors affecting traffic models from different aspects such as metropolitan planning according to the present conditions can help high-level decision-makers and also, at the micro-level, help the travelers to make appropriate decisions for scheduling affairs, route selection, and vehicle type selection. Given the importance of this topic, a framework will be presented in this study that will evaluate the impact of some identified factors such as travel distance, climate, and urban events, and then all these factors will be presented in mathematical formulas. In the end, based on the model, the travel time will be predicted. In this framework, gene expression... 

Businesses need to narrow down their segmentation and divide customers into smaller groups because some low-loyalty customers have the potential to become loyal customers. Therefore, we have focused on non-loyal customers. This study proposes a framework in which customers with low loyalty value are examined in four six-month time periods and an overall two-year time period and divided into smaller periods. First, a clustering was performed with K-Means algorithm and RFM model, then the migration pattern of customers from one cluster to another was identified using the sequential pattern technique. The transition of customers and their potentiality was determined and according to these... 

Since physical variables are almost dependent, uncertain and have significant time and spatial changes, nature of hydrological and environmental systems is so complicated, nonlinear and dynamic. Many models have been developed for studying and analyzing various phenomena in these systems. Recently data mining approaches have been used as new methods for modeling of complicated engineering systems. In this study, performance of several data mining tools in analyzing two phenomena in water quality management have been evaluated. These algorithms are Multilayer Perceptron Neural Network (MLP), Radial Basis Function Neural Network (RBFN), Support Vector Machines (SVMs) in the field of Artificial... 

With the rapid development of the economy, more people have started investing in the stock market. Predicting price changes can reduce the risk of investing in stocks. Technical data such as price and volume in the stock market is usually used to predict stock prices, and less often other types of data such as market data or fundamental data are used. In this study, we want to determine what impact each of the available data types has on stock prices. For example, data of buy and sell for per capita, capital inflows and outflows for small and large natural and legal investors, information related to the stocks themselves, indicators, fundamental data such as earnings per share (EPS) and...