Branch Client Behavior Analysis Using RFM Method

In today's competitive world, applying new techniques to business development has a great impact. The restaurant industry is no exception. Therefore, in this research, using new methods of knowledge discovery and data mining, customer data of chain restaurant is investigated. The purpose of this study was to explore customer behavior patterns using data mining methods.In this study, one million and five hundred thousand customer records were reviewed in five branches of a chain restaurant and two stages of clustering modeling using RFM method and then classification modeling were performed on the data and the behavior rules chain restaurant customers were extracted. The results of this study have helped to identify the loyal and profitable customers of the chain restaurant which has led to the improvement of the profitability of the chain restaurant. One of the innovations of this research has been the communication between clustering and classification results

Model Design for Personnel Selection with Data Mining Approach (Case Study: A Commerce Bank of Iran)

The success or failure of an organization has a direct relationship with how its human resources are employed and retained. It is the case that organizations keep large amounts of information and data on entrance evaluations and processes. This information, however, is often left unutilized. Data mining is considered a solution for analyzing these data. This paper is investigating educated and objective methods of data analysis. It follows statistical rules, data mining techniques, and the relationship between entrance evaluation scores and personal and professional variables. These factors are studied in order to determine the assignment and rank of potential employees. The database and personnel information of the a Commerce Bank of Iran (in years of 2005 and 2006) is studied and analyzed as a case study in order to identify the labor factors which are considered effective in job performance. The data mining technique that is used in this project serves as the decision-tree. Rules Derivation has been accomplished by the QUEST, CHAID, C5.0 and CART algorithms. The objective and the appropriate algorithms are determined based on seemingly “irrelevant” components, which the Commerce Bank Human Resources management experts described. Results indicated not taking into account the “performance assessment” variable as the objective. Also this project has identified the following from 26 variables have been investigated, five variables as the effective factors in employee promotion: examination score, interview score, degree, years of experience, and job location. The paper's results led in knowledge that can be practical

Reliability-Based Life Cycle Assessment of the Concrete Slab in Bridges

To guarantee the safety and serviceability of present bridges maintenances are essential all around the Globe and most governments’ budget effect by overhaul such infrastructures yearly without any rate of interest in these maintenances. A large proportion of costs are allocated for maintenances costs due to the increasing number of present bridges. Maintenances also have a substantial effect on economic and sustainable development. In this study, a durability reliability analysis of reinforced concrete slabs subject to the action of deteriorations during the life cycle of bridges is presented. This paper is focused on providing insight into the role of reliability life cycle assessment in One Century of bridge maintenance cost performance by two reliability factors in the monitoring of 150 points of modeled bridge. The first factor in this model is crack size expansion about the time scale of the concrete deck by consideration of concrete deterioration. The second factor is the reliability of the compaction ratio of concrete (fc) based on the probability that this factor will perform as intended for 100 years duration under normal conditions. Finally, this paper generates a complicated model, which illustrates specific duration need maintenance to reach high performance in a total of 100 years. For instance, by this sample and data binding, the best time to get reliable and durable structure is in 23 years of initial usage, and this bridge needs concrete structural rehabilitation to reach high performance and durable concrete deck and pavement in the life cycle of the bridge

Implementing a Data Mining Solution Approach to Identify the Valuable Customers for Facilitating Electronic Banking

Nowadays, the banking system is known as one of the inherent sectors of customer relationship management systems. Its main advantage is to redesign a more responsive organization to satisfy the customers. The banking system aims to improve the structure of organizations to provide a better customer service through a set of automated and integrated processes. The final goal is to collect and reprocess the personal information of customers. To handle this dilemma, a number of new techniques in data mining provide a powerful tool to explore customers’ information regarding a set of data and tools for customer relationship management. Accordingly, the customers’ classification and coordination of banking system are the main challenging issues of today's world. These reasons motivate the attempts of this study to apply a composition of neural network by considering the C4.5 decision tree and the k-closest neighbor method as a variant of core boosting methodology with maximal strategy. To validate the proposed solution approach, a case study of Ansar Bank in Iran is utilized. From the results, it is observed that the proposed method provides a competitive output with the rate of 95% for the customers’ classification. It also outperforms other existing methods with the rate of C4.5 decision tree, neural network, Naive Bayes and KNN with the rate of 1.04%. The main finding of this research is to propose an algorithm with the error rate of 1.9% and error squared of 0.72% as the best performance among other methods from the literature

Implementing a Data Mining Solution Approach to Identify the Valuable Customers for Facilitating Electronic Banking

Nowadays, the banking system is known as one of the inherent sectors of customer relationship management systems. Its main advantage is to redesign a more responsive organization to satisfy the customers. The banking system aims to improve the structure of organizations to provide a better customer service through a set of automated and integrated processes. The final goal is to collect and reprocess the personal information of customers. To handle this dilemma, a number of new techniques in data mining provide a powerful tool to explore customers’ information regarding a set of data and tools for customer relationship management. Accordingly, the customers’ classification and coordination of banking system are the main challenging issues of today's world. These reasons motivate the attempts of this study to apply a composition of neural network by considering the C4.5 decision tree and the k-closest neighbor method as a variant of core boosting methodology with maximal strategy. To validate the proposed solution approach, a case study of Ansar Bank in Iran is utilized. From the results, it is observed that the proposed method provides a competitive output with the rate of 95% for the customers’ classification. It also outperforms other existing methods with the rate of C4.5 decision tree, neural network, Naive Bayes and KNN with the rate of 1.04%. The main finding of this research is to propose an algorithm with the error rate of 1.9% and error squared of 0.72% as the best performance among other methods from the literature.</p